Publications (2)6.3 Total impact
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Article: Activity of 2 methoxyestradiol (Panzem NCD) in advanced, platinum-resistant ovarian cancer and primary peritoneal carcinomatosis: a Hoosier Oncology Group trial.
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ABSTRACT: 2-Methoxyestradiol (Panzem, 2ME2) is an endogenous metabolite of estradiol that destabilizes microtubules and exerts anti-angiogenic properties. This study was conducted to determine the activity and safety of 2ME2 administered as a NanoCrystal dispersion (NCD) formulation in patients with recurrent, platinum-resistant epithelial ovarian cancer (EOC). Eligible patients had relapsed, platinum-resistant or refractory EOC with measurable or detectable disease. There was no limit on the number of prior treatment regimens. 2ME2 NCD 1000 mg orally four times daily (q.i.d.) was administered continuously during 4 week cycles. The primary endpoint was objective response rate (ORR). Secondary endpoints were assessment of toxicity, rate of clinical benefit defined as the number of patients experiencing an objective response, a CA125 response or stable disease (SD) >3 months, mean change in CA-125, progression-free survival (PFS), and pharmacokinetic analyses of 2ME2. Eighteen patients were enrolled. Median age was 65.5 (range 40-73). Patients had received a median of five prior treatments. The most common adverse events were fatigue (78%), nausea (78%), diarrhea (39%), neuropathy (50%), edema (39%), and dyspnea (44%), the majority being grade 1-2. There were no objective responses, but seven patients had SD as best response. Of those, two patients had SD for greater than 12 months. The rate of clinical benefit was 31.3%. Fairly stable plasma levels of 2ME2 ranging within the predicted therapeutic window were observed. The NCD formulation of 2ME2 is well tolerated in patients with heavily pretreated EOC. Few of these heavily pretreated patients had sustained stable disease.Gynecologic Oncology 08/2009; 115(1):90-6. · 3.89 Impact Factor -
Article: A simple error classification system for understanding sources of error in automatic speech recognition and human transcription.
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ABSTRACT: To (1) discover the types of errors most commonly found in clinical notes that are generated either using automatic speech recognition (ASR) or via human transcription and (2) to develop efficient rules for classifying these errors based on the categories found in (1). The purpose of classifying errors into categories is to understand the underlying processes that generate these errors, so that measures can be taken to improve these processes. We integrated the Dragon NaturallySpeaking v4.0 speech recognition engine into the Regenstrief Medical Record System. We captured the text output of the speech engine prior to error correction by the speaker. We also acquired a set of human transcribed but uncorrected notes for comparison. We then attempted to error correct these notes based on looking at the context alone. Initially, three domain experts independently examined 104 ASR notes (containing 29,144 words) generated by a single speaker and 44 human transcribed notes (containing 14,199 words) generated by multiple speakers for errors. Collaborative group sessions were subsequently held where error categorizes were determined and rules developed and incrementally refined for systematically examining the notes and classifying errors. We found that the errors could be classified into nine categories: (1) announciation errors occurring due to speaker mispronounciation, (2) dictionary errors resulting from missing terms, (3) suffix errors caused by misrecognition of appropriate tenses of a word, (4) added words, (5) deleted words, (6) homonym errors resulting from substitution of a phonetically identical word, (7) spelling errors, (8) nonsense errors, words/phrases whose meaning could not be appreciated by examining just the context, and (9) critical errors, words/phrases where a reader of a note could potentially misunderstand the concept that was related by the speaker. A simple method is presented for examining errors in transcribed documents and classifying these errors into meaningful and useful categories. Such a classification can potentially help pinpoint sources of such errors so that measures (such as better training of the speaker and improved dictionary and language modeling) can be taken to optimize the error rates.International Journal of Medical Informatics 10/2004; 73(9-10):719-30. · 2.41 Impact Factor
