Concussion symptom inventory: an empirically derived scale for monitoring resolution of symptoms following sport-related concussion.
ABSTRACT Self-report post-concussion symptom scales have been a key method for monitoring recovery from sport-related concussion, to assist in medical management, and return-to-play decision-making. To date, however, item selection and scaling metrics for these instruments have been based solely upon clinical judgment, and no one scale has been identified as the "gold standard". We analyzed a large set of data from existing scales obtained from three separate case-control studies in order to derive a sensitive and efficient scale for this application by eliminating items that were found to be insensitive to concussion. Baseline data from symptom checklists including a total of 27 symptom variables were collected from a total of 16,350 high school and college athletes. Follow-up data were obtained from 641 athletes who subsequently incurred a concussion. Symptom checklists were administered at baseline (preseason), immediately post-concussion, post-game, and at 1, 3, and 5 days post-injury. Effect-size analyses resulted in the retention of only 12 of the 27 variables. Receiver-operating characteristic analyses were used to confirm that the reduction in items did not reduce sensitivity or specificity. The newly derived Concussion Symptom Inventory is presented and recommended as a research and clinical tool for monitoring recovery from sport-related concussion.
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ABSTRACT: Background There is an urgent need for objective criteria adjunctive to standard clinical assessment of acute Traumatic Brain Injury (TBI). Details of the development of a quantitative index to identify structural brain injury based on brain electrical activity will be described. Methods Acute closed head injured and normal patients (n=1470) were recruited from 16 US Emergency Departments and evaluated using brain electrical activity (EEG) recorded from forehead electrodes. Patients had high GSC (median=15), and most presented with low suspicion of brain injury. Patients were divided into a CT positive (CT+) group and a group with CT negative findings or where CT scans were not ordered according to standard assessment (CT-/CT_NR). Three different classifier methodologies, Ensemble Harmony, Least Absolute Shrinkage and Selection Operator (LASSO), and Genetic Algorithm (GA), were utilized. Results Similar performance accuracy was obtained for all three methodologies with an average sensitivity/specificity of 97.5%/59.5%, area under the curves (AUC) of 0.90 and average Negative Predictive Validity (NPV) >99%. Sensitivity was highest for CT+ cases with potentially life threatening hematomas, where two of three classifiers were 100%. Conclusion Similar performance of these classifiers suggests that the optimal separation of the populations was obtained given the overlap of the underlying distributions of features of brain activity. High sensitivity to CT+ injuries (highest in hematomas) and specificity significantly higher than that obtained using ED guidelines for imaging, supports the enhanced clinical utility of this technology and suggests the potential role in the objective, rapid and more optimal triage of TBI patients.Computers in Biology and Medicine 08/2014; · 1.48 Impact Factor
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ABSTRACT: The purpose of this study was to validate a recently proposed return-to-play (RTP) decision model that simplifies the complex process into three underlying constructs: injury type and severity, sport injury risk, and factors unrelated to injury risk (decision modifiers). We used a cross-over design and provided clinical vignettes to clinicians involved in RTP decision making through an online survey. Each vignette included examples changing injury severity, sport risk (e.g. different positions), and non-injury risk factors (e.g. financial considerations). As the three-step model suggests, clinicians increased restrictions as injury severity increased, and also changed RTP decisions when factors related to sport risk and factors unrelated to sport risk were changed. The effect was different for different injury severities and clinical cases, suggesting context dependency. The model was also consistent with recommendations made by subgroups of clinicians: sport medicine physicians, non-sport medicine physicians, and allied health care workers.Scandinavian Journal of Medicine and Science in Sports 09/2014; · 3.21 Impact Factor
Concussion Symptom Inventory: an empirically derived scale for
monitoring resolution of symptoms following sport-related concussion
Christopher Randolpha,*, Scott Millisb, William B. Barrc,d, Michael McCreae,f, Kevin M. Guskiewiczg,h,
Thomas A. Hammekef, James P. Kellyi,j
aDepartment of Neurology, Loyola University Medical Center, Chicago (Maywood), IL, USA
bDepartment of Physical Medicine and Rehabilitation, Wayne State University School of Medicine, Detroit, MI, USA
cDepartment of Neurology, New York University School of Medicine, New York, NY, USA
dDepartment of Psychiatry, New York University School of Medicine, New York, NY, USA
eNeuroscience Center, Waukesha Memorial Hospital, Waukesha, WI, USA
fDepartment of Neurology, Medical College of Wisconsin, Milwaukee, WI, USA
gDepartment of Exercise and Sport Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
hDepartment of Orthopedics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
iDepartment of Neurosurgery, University of Colorado Denver School of Medicine, Denver, CO, USA
jDepartment of Physical Medicine and Rehabilitation, University of Colorado Denver School of Medicine, Denver, CO, USA
Accepted 27 May 2009
Self-report post-concussion symptom scales have been a key method for monitoring recovery from sport-related concussion, to assist in
medical management, and return-to-play decision-making. To date, however, item selection and scaling metrics for these instruments have
been based solely upon clinical judgment, and no one scale has been identified as the “gold standard”. We analyzed a large set of data from
existing scales obtained from three separate case–control studies in order to derive a sensitive and efficient scale for this application by elim-
inating items that were found to be insensitive to concussion. Baseline data from symptom checklists including a total of 27 symptom vari-
ables were collected from a total of 16,350 high school and college athletes. Follow-up data were obtained from 641 athletes who
subsequently incurred a concussion. Symptom checklists were administered at baseline (preseason), immediately post-concussion, post-
game, and at 1, 3, and 5 days post-injury. Effect-size analyses resulted in the retention of only 12 of the 27 variables. Receiver-operating
characteristic analyses were used to confirm that the reduction in items did not reduce sensitivity or specificity. The newly derived
Concussion Symptom Inventory is presented and recommended as a research and clinical tool for monitoring recovery from sport-related
Keywords: Brain injury; Post-concussion; Scale
The medical management of sport-related concussion has suffered from a dearth of empirical data from prospective
controlled outcome studies. This has led to a burgeoning number of conflicting injury classification systems and
return-to-play guidelines. Although there are now well over a dozen different proposed sets of guidelines, it has been
recognized that few, if any, of these are evidence-based, and none has been universally accepted (Aubry et al., 2002;
Guskiewicz et al., 2004). The various guidelines are all in agreement, however, that a player should be symptom-free
*Corresponding author at: 1 East Erie, Suite 355, Chicago, IL 60611, USA. Tel.: þ1-708-216-3539; fax: þ1-708-216-4629.
E-mail address: email@example.com (C. Randolph).
# The Author 2009. Published by Oxford University Press on behalf of the National Academy of Neuropsychology. All rights reserved.
For permissions, please e-mail: firstname.lastname@example.org.
Archives of Clinical Neuropsychology Advance Access published June 23, 2009
before returning to play (Cantu, 1998; Kelly et al., 1991; Kelly & Rosenberg, 1997; LeBlanc, 1998). Although the
rationale for this recommendation also remains poorly substantiated by any evidence to date, the primary concern is
that players may be at an elevated risk of repeat concussion during the symptomatic post-concussive period. There is
some preliminary evidence that such a period of vulnerability may exist, and that recovery following a second concus-
sion may be somewhat more prolonged (Guskiewicz et al., 2003). In prospective controlled studies, the risk of a second
concussion within the same season in American football has been reported to range from approximately 3%–6% for
players who suffer an initial concussion (Guskiewicz et al., 2003; Macciocchi, Barth, Littlefield, & Cantu, 2001). A
second concern is the risk of delayed brain swelling, or “second-impact syndrome”. This can be a life-threatening con-
dition, but it is extremely rare. There are less than 20 documented cases in the world literature to date, the causative
mechanism remains unclear, and it can occur without a second injury (McCrory, 2001; McCrory & Berkovic, 1998;
Mori, Katayama, & Kawamata, 2006).
There is a general consensus, however, that until these risks are clarified, concussed players should be free of the
residual effects of concussion before returning to competition. A number of methods have been explored to measure
concussion-related symptoms or impairments, including brief “sideline” neurocognitive examinations (McCrea, 2001;
McCrea, Kelly, Kluge, Ackley, & Randolph, 1997; McCrea, Kelly, Randolph, Cisler, & Berger, 2002), balance
testing (Guskiewicz, 2001, 2003; Guskiewicz, Ross, & Marshall, 2001), and more extensive neuropsychological
testing (Barr, 2001; Bleiberg et al., 2004; Echemendia & Julian, 2001; Erlanger et al., 2003; Hinton-Bayre & Geffen,
2002; Lovell & Collins, 1998; Macciocchi, Barth, Alves, Rimel, & Jane, 1996; Peterson, Ferrara, Mrazik, Piland, &
Elliot, 2003; Randolph, 2001) designed to detect changes in cognitive functioning by comparing players with their
own preseason baseline. The use of self-report subjective symptom checklists or scales has also been a consistent com-
ponent of concussion management, and these have repeatedly been demonstrated to be sensitive to the effects of con-
cussion (Macciocchi et al., 1996; Maroon et al., 2000; McCrea et al., 2003; McCrory, Ariens, & Berkovic, 2000;
Peterson et al., 2003).
Self-report symptoms are also the primary decision-making factor in the most commonly used guidelines for
return-to-play. Concussed athletes typically show elevated scores on symptom concussion checklists for at least as
long as impairment is detectable via more time-consuming and expensive methodologies (e.g., neuropsychological
testing), despite concerns that players might under-report symptoms in order to be cleared to return-to-play
(Peterson et al., 2003; Randolph, McCrea, & Barr, 2005). In addition, recent publications, including a consensus
paper, have recommended that players should be asymptomatic before screening for impairment using any type of
neuropsychological testing (McCrea et al., 2005; McCrory et al., 2005). Finally, serious doubts have been raised
regarding the reliability and incremental utility of neuropsychological testing in detecting recovery from sport-related
concussion (Randolph et al., 2005). In a recent study that was the first to explore the use of computerized neurocog-
nitive tests utilizing “real-world” retest intervals to measure test reliability, the stability coefficients of these measures
proved to be extremely poor (Broglio, Ferrara, Macciocchi, Baumgartner, & Elliott, 2007). For the most widely used
of these tests (ImPACT), stability coefficients ranged from only 0.15 to 0.39, with an average of 0.29. This is far
below the level of stability needed for individual decision-making (usually recommended to be at least above 0.8),
and suggests that these instruments lack sufficient reliability to be of use in establishing cognitive recovery. This
type of finding further underscores the central role of subjective symptom checklists in monitoring recovery from
A variety of subjective symptom scales have been used in the study of sport-related concussion, although these typically
involve substantial overlap in item content, which has been chosen to date on the basis of clinical experience with
concussion-related symptoms. The overall sensitivity of these scales to the effects of sport-related concussion has been
repeatedly demonstrated (Barth et al., 1989; Erlanger et al., 2001; Lovell & Collins, 1998; Lovell et al., 2003; McCrea
et al., 2003; McCrory et al., 2000; Mrazik et al., 2000). Until recently, however, the psychometric properties of these check-
lists/scales have remained largely unexamined. In a recent study, data from one of these scales were reported for approxi-
mately 1700 high school and college athletes, and compared with data from a concussed sample of 260 athletes surveyed
within five days of injury (Lovell et al., 2006). This paper provided only descriptive statistics regarding the scale, did not
involve a prospective controlled study, and did not explore the relative utility of individual items in differentiating con-
cussed from non-concussed athletes. Piland, Motl, Ferrara, and Peterson (2003) reviewed data from a group of 279
college athletes who were administered a 16-item symptom scale at baseline to explore the factor structure of the scale,
which was hypothesized to consist of three relatively distinct domains (Piland et al., 2003). They eliminated seven
items, primarily on the basis of face/content validity, and achieved a better fit to their model. Clinical validity was explored
with a small sample of concussed players (N ¼ 17).
2Randolph et al. / Archives of Clinical Neuropsychology
Although this latter study involved a sophisticated approach to exploring certain psychometric properties of a concussion
symptom scale, the primary application of a symptom scale in the medical management of sport-related concussion is in the
efficient and sensitive detection of the effects of concussion, as opposed to the characterization of these effects. In this context,
item selection should be driven primarily by sensitivity to concussion, requiring an empirical approach to determine item reten-
tion. In addition, the study of Piland and colleagues did not explore the scaling characteristics of their instrument, perhaps
because of the relatively small number of injured players in their sample. As a result, it remains unclear which symptoms
are actually sensitive to the effects of concussion, and whether or not a 7-point Likert-type scale is necessary for the detection
and tracking of concussion-related symptomatology.
We recently completed three separate studies, involving the use of largely overlapping symptom scales, with data on over
16,000 athletes at baseline and over 600 athletes following concussion. We combined these datasets in order to enable an
empirical study of each item’s value to the scale. The purpose of this paper was to derive the most sensitive and efficient
scale possible for the detection and tracking of self-reported symptoms following sport-related concussion.
Materials and Methods
The data for this study were derived from three separate projects: The Concussion Prevention Initiative (CPI), the NCAA
Concussion Study (NCAA), and the Project Sideline (Sideline). The protocols and subject inclusion for each of these projects
are described subsequently. The symptom scales employed in each project are contained in Table 1. Each symptom, in each
project, was scored on a 7-point Likert-type scale from 0 (absent) to 6 (severe). Although the symptom scales for each project
differed slightly, they did have substantial overlap with one another, and with symptom scales used in earlier studies (Lovell &
Collins, 1998; Macciocchi et al., 1996).
Table 1. Symptoms included in scales for each of the three projects
Sleeping more than usual
Feeling like “in a fog”
Sensitivity to light
Sensitivity to noise
Feeling slowed down
Trouble falling asleep
Sleeping more than usual
Feeling like “in a fog”
Sensitivity to light/noise
Sleeping more than usual
Feeling like “in a fog”
Sensitivity to light
Sensitivity to noise
Feeling slowed down
Burning feeling in feeta
Notes: CPI ¼ Concussion Prevention Initiative; NCAA ¼ the NCAA Concussion Study; Sideline ¼ the Project Sideline.
aIncluded as tests of valid responding/specificity. For all three studies, symptoms were recorded on a 7-point Likert-type scale, with scores ranging from
0 (absent) to 6 (severe).
Randolph et al. / Archives of Clinical Neuropsychology3
Concussion Prevention Initiative
This project involved the collection of prospective data from 14 colleges and 110 high schools from 2000 to 2003, invol-
ving athletes from football, men’s soccer, women’s soccer, men’s lacrosse, women’s lacrosse, men’s ice hockey, and
women’s ice hockey. The total number of athletes examined at baseline was 9,094 (72.7% male), with 375 subsequent con-
cussions. The data from this project have not yet been published, and the symptom checklist was only one component of this
study. Detection of concussion was made on a clinical basis in accordance with procedures followed by the NCAA and
Sideline studies (essentially, evidence of an alteration in mental status as the result of a mechanical insult to the head),
NCAA Concussion Study
This study involved 4,238 male football players from 15 US colleges. All players underwent preseason baseline testing in
1999, 2000, and 2001. There were 196 subsequent concussions, with assessments points at the time of injury, 3 hr post-injury,
and at 1, 2, 3, 5, 7, and 90 days post-injury. Portions of the data from this study have been reported elsewhere (Guskiewicz
et al., 2003; McCrea et al., 2003).
This Milwaukee-based project began in 2000 and involved a total of 18 high schools in the southeastern Wisconsin area,
including athletes from football, hockey, and soccer teams. The baseline sample included a total of 3,018 athletes (97%
male), with a total of 70 subsequent concussions. Portions of these data have been presented elsewhere (McCrea,
Hammeke, Olsen, Leo, & Guskiewicz, 2004). The overall concussion rate across studies (2%–5% per season) is consistent
with epidemiological survey data (Powell & Barber-Foss, 1999).
There were a total of five post-injury assessments that were common to all three studies: Immediately post injury,
post-game (approximate 3 hr post injury), Day 1, Day 3, and Day 5. The primary analysis was designed to eliminate
any items that proved to be insensitive to concussion. The criterion for retention was an effect size of at least 0.3 on
at least two of the five post-injury assessment points. This essentially requires an increase in the average level of symp-
tomatology of 0.3 SD over the baseline mean for that item. The baseline means for all items were less than 0.5 on the
7-point Likert scale (0–6), and the standard deviations associated with these means ranged from 0.3 to 1.0. Achieving
this retention criterion, therefore, required a very modest increase over baseline level of symptomatology for any vari-
able, an effect size increase of 0.3 over baseline could be achieved with a mean rating score at that assessment point that
was still less than 1. Given this rather liberal retention criterion, we felt that requiring this effect size to be reached on at
least two assessment points was an adequately conservative approach to preclude retaining a variable as the result of a
spurious finding (false-positive).
Applying this retention rule (an effect size change of 0.3 from baseline on at least two observations) resulted in the elim-
ination of 13 variables, leaving the following 14 variables: Headache, nausea, balance/dizziness, fatigue, drowsiness, feeling
slowed down, in a fog, difficulty concentrating, difficulty remembering, neck pain, blurred vision, sensitivity to light, sensi-
tivity to noise, and sensitivity to light/noise. Because sensitivity to light and sensitivity to noise were independently sensitive,
and these variables were combined in only the NCAA dataset, sensitivity to light/noise was also eliminated as a separate vari-
able. In addition, it seemed likely to us that neck pain was attributable to cervical strain and not a direct result of concussion; as
a result, this variable was eliminated as well, leaving a total of 12 symptoms.
Rasch rating scale analysis (Linacre, 2004; Rasch, 1960), one of the models within the Rasch measurement family, was used
to explore the utility of the 7-point Likert scale. There were several indicators suggesting that therewas insufficient information
4 Randolph et al. / Archives of Clinical Neuropsychology
in the data to yield reliable parameter estimates if a 7-point scale were used; for example, (a) a number of rating categories had
less than 10 observations; (b) irregularity in observation frequency across categories was found that signaled aberrant category
endorsement by subjects; (c) average measures did not advance monotonically with category; and (d) some step categories
advanced by less than 1.4 logits, whereas others advanced by more than 5.0 logits. These findings suggested that the
number of categories could be reduced below 7 for most of the remaining 12 variables. After a great deal of discussion,
however, a decision was made to retain the original Likert scaling.
This decision was made on several bases. First, if the number of categories was reduced for each item, it would require an
assumption about how the players in the study would have responded to a scale with a more limited range (e.g., would a
response of 1 on the original 7-point scale remained a 1 if the scale became dichotomous, or might that player now
respond with a 0?). The only alternative to this assumption would be re-validating the new scaling with a new sample of
players. It was ultimately concluded that this assumption was not one that could be comfortably made, and the labor-intensive
nature of these studies makes a follow-up validation project with a reasonable sample size rather impractical. In addition, the
use of a Likert-type scaling to monitor symptom recovery had an intuitive appeal to the clinicians in our group, who felt that the
information regarding symptom severity might have clinical significance in some cases (e.g., in detecting a worsening head-
ache in the rare player who suffers a delayed deterioration in neurological status). Finally, some of the items in the scale did
seem to appear to meet assumptions for 7-point scaling, suggesting that reducing the scaling for these items might result in
some loss of information.
Receiver-Operating Characteristic Curves
To ensure that we did not lose substantial sensitivity by eliminating items, we conducted receiver-operating characteristic
(ROC) analyses of data from two post-injury assessment points: Immediately post-injury and Day 5 post-injury. Scores for all
concussed players using both the original scales and the newly derived 12-item scale were compared with the scores for the
entire baseline sample on the original scales.
Although sensitivity and specificity are commonly used to assess the diagnostic efficiency of tests, both sensitivity and
specificity rely on a single cut-off score. A more complete description of classification accuracy is given by the area under
the ROC curve (AUC) (Zhou, Obuchowski, & McClish, 2002). The curve plots the probability of detecting a disorder (sensi-
tivity) and false signal (1—specificity or false positive) for an entire range of possible cut-off scores (Hsiao, Bartko, & Potter,
1989). The AUC provides a measure of the model to discriminate between persons with a disorder versus people without
the disorder. Perfect discrimination is achieved at an AUC of 1.00, with chance falling at an AUC of 0.50, represented as
the area under the diagonal line traversing from zero false-positive rate, and zero sensitivity, to perfect sensitivity and
100% false-positives. AUC of 0.7–0.79 have been characterized as acceptable, 0.8–0.89 as excellent, and 0.9 or above as
There are different methods to calculate AUCs. Parametric methods are based on the bivariate normal distribution,
which assume a normal distribution for cases with the disease and a normal distribution for cases without, or that the
data have been monotonically transformed to normal. Parametric methods also assume homoscedasticity. The assump-
tions can be restrictive and thus, we elected to use a non-parametric approach (DeLong, DeLong, & Clarke-Pearson,
Fig. 1 shows the ROC curves for both the CSI Day 1 and the original full-scale Day 1. They are nearly identical. CSI Day 1
had an AUC of 0.867 (95% CI .85, .88), whereas the full-scale Day 1 had an AUC of .871 (95% CI .85, .89). Both tests showed
excellent diagnostic discrimination. Fig. 2 shows the ROC curves for both the CSI Day 5 and the full-scale Day 5. Again, both
are essentially identical. CSI Day 5 had an AUC of .689 (95% CI .67, .71), whereas the full-scale Day 5 had an AUC of .71
(95% CI .69, .73). Not surprisingly, the diagnostic discrimination is poorer on Day 5 than on Day 1, as symptoms have sub-
stantially resolved by the fifth day post-injury.
Descriptive statistics are presented for the concussed sample at all common assessment points for the new 12-item scale,
which we have termed the Concussion Symptom Inventory (CSI) in Table 2, and graphically depicted in Fig. 3. Scores
were significantly elevated from the immediate post-injury assessment point through Day 3, returning to baseline levels by
Randolph et al. / Archives of Clinical Neuropsychology5
Discussion: Concussion Symptom Inventory
Self-report post-concussion symptom scales have been a key methodology in monitoring recovery from sport-related con-
cussion, and assisting in return-to-play decision-making. To date, however, the scales employed for this purpose have been
constructed on the basis of clinical judgment and no single scale has been identified as a “gold standard” for this purpose.
The newly derived Concussion Symptom Inventory (CSI) is presented in the Appendix. To our knowledge, this is the first
scale that has been empirically derived for the purpose of monitoring subjective symptoms following concussion. The
source data also constitute the largest sample of prospectively studied cases of concussion in the literature to date, with a con-
cussed sample size of 641 athletes compared with a baseline sample of 16,350 athletes. We have elected to include space on the
form for the scale for the recording of any additional subjectively reported symptom that a clinician believes may have been
due to the concussion. This will allow for a full clinical documentation of subjective symptomatology, including symptoms that
were very rarely reported (or not queried) in our prospective studies. We are not proposing specific “cut-off” scores, because we
lack sufficient empirical data at this point to suggest that there actually is a quantifiable risk of returning a player to competition
based upon a particular CSI score. This is, of course, true of any symptom scale or other technique for measuring impairment
We propose that athletic trainers and team medical personnel employ the CSI as a standardized methodology for tracking
symptom resolution following concussion, and incorporate the information from the CSI into clinical decision-making regard-
ing return-to-play. This recommendation is appropriate, given the lack of empirically derived alternative scales to date.
The decision-making process regarding return-to-play should be informed by the evolving literature on the natural history
and outcome of sport-related concussion, and by the specific clinical circumstances of the individual player. It is important
to emphasize that the CSI is not intended to constitute the sole basis for clinical decision-making in the medical management
of sport-related concussion, and that individual players may also experience concussion-related symptoms (e.g., sleep
disturbance) that are not recorded within the CSI owing to the relative infrequency with which they occurred in our concussed
Fig. 1. Receiver-operating characteristic curves comparing the newly derived 12-item Concussion Symptom Inventory (CSI) scale to the full scale (abbre-
viated GSC for Graded Symptom Checklist). Baseline scores for the entire sample were compared with scores immediately post injury for the 641 concussed
athletes. There is virtually complete overlap between the scales, suggesting that the 12-item CSI is as effective as the full GSC in detecting the effects of con-
cussion at this time point.
6 Randolph et al. / Archives of Clinical Neuropsychology
sample. We do not intend for athletic trainers or team medical personnel to rely solely upon the results of the CSI to determine
The CSI does, however, provide an empirically based, rapid, and systematic methodology for tracking subjective symptoms
following sport-related concussion. To date, a variety of symptom scales have been used in clinical assessments and studies of
the natural history of concussion, and item selection and scaling have been driven by clinical judgment rather than empirical
data. This large sample of players with baseline and post-concussion data allowed us to eliminate a number of items that proved
to be largely insensitive to concussion.
The risks of “premature” return-to-play following sport-related concussions are as yet poorly delineated, and none of the
many guidelines that have been promulgated for this purpose are evidence-based. They are all in agreement, however, that
players should be symptom-free before being cleared to return. This would seem to be a reasonable and appropriately conser-
vative approach to concussion management, particularly in younger athletes, until additional data regarding risks are accrued
and clinical decision-making can be driven by reliable evidence. The CSI constitutes a relatively rapid, standardized way of
Table 2. Descriptive statistics on the Concussion Symptom Inventory for the 641 concussed athletes
Assessment MinimumMaximumMean SD
Day 1 post-injury
Day 3 post-injury
Day 5 post-injury
Fig. 2. Receiver-operating characteristic curves comparing the newly derived 12-item Concussion Symptom Inventory (CSI) scale to the full scale (abbre-
viated GSC for Graded Symptom Checklist). Baseline scores for the entire samplewere compared with scores at Day 5 postinjury for the 41 concussed athletes.
Although discriminability is reduced compared with the immediate post-injury assessment for both scales, they are again comparable in terms of area under the
curve (see text), suggesting that the 12-item CSI is as effective as the full GSC in detecting the effects of concussion at this time point.
Randolph et al. / Archives of Clinical Neuropsychology7
monitoring symptom recovery, with no loss in sensitivity or reliability in comparison with much longer inventories. It has been
demonstrated that concussed athletes routinely endorse subjective symptoms for at least as long as impairment is typically
detectable by more time-consuming and expensive methodologies (e.g., neuropsychological testing), despite concerns that
players might under-report symptoms in order to be cleared to return-to-play. Finally, recent publications, including
a consensus paper, have recommended that players should be asymptomatic before screening for impairment using any
type of neuropsychological testing, further underscoring the central role of subjective symptom checklists in monitoring recov-
ery from concussion.
Although the CSI was derived from sport-related concussion data and the primary utility of the scale is intended to be
for this purpose, it is conceivable that the scale might also prove useful in studies of the natural history of concussion due
to other causes. To date, there is no clear consensus regarding a specific scale for use in tracking recovery of symptoms
from concussion/mild traumatic brain injury in clinical (non-athletic) populations. Although the CSI has not yet been validated
for applications outside of sports, it may have some appeal owing to the fact that it was empirically derived from data
obtained from injured athletes. This population is typically highly motivated to recover, and therefore would be unlikely to
over-endorse symptoms as a result of psychological factors (e.g., depression, somatoform tendencies, malingering).
Although the CSI is clearly composed of a number of symptoms that are not exclusively specific to concussion (e.g., headache,
drowsiness), it is reasonably safe to assume that the symptoms that were retained for this scale were in fact generated by con-
cussion and not by other factors that might be operating in some proportion of mild TBI patients recruited from a non-sports
This project was supported in part by funding from the NCAA, the National Operating Committee on Standards for Athletic
Equipment (NOCSAE), Center for Disease Control’s National Center for Injury Prevention and Control (NPIPC), National
Academy of Neuropsychology, Waukesha Memorial Hospital Foundation, National Federation of State High School
Associations, NFL Charities, Green Bay Packer Foundation, Milwaukee Bucks, Herbert H. Kohl Charities, Waukesha
Service Club, and the Medical College of Wisconsin General Clinical Research Center (M01-RR00058 from the National
Institutes of Health).
Conflict of Interest:
The authors would also like to acknowledge the invaluable assistance of Amy Mathews, MSW, and Stephen Marshall, PhD,
in data management.
Fig. 3. Mean Concussion Symptom Inventory (CSI) scores for the 641 concussed athletes at baseline and post-injury assessments. Asterisk (*) indicates sig-
nificant difference from baseline on matched-pair t-tests (see Table 2 for additional descriptive statistics).
8 Randolph et al. / Archives of Clinical Neuropsychology
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