Analyzing Knowledge Management Systems: A Veritistic Approach.
Bibliographic record
Abstract
The optimal dose and schedule for paclitaxel (Taxol; Bristol-Myers Squibb Company, Princeton, NJ) in the treatment of patients with advanced breast cancer are not known. Based on our phase I study in non-small cell lung cancer, in which the dose intensity of paclitaxel was successfully escalated by using a weekly schedule, we initiated a phase II study of weekly paclitaxel in previously untreated patients with metastatic breast cancer (MBC) and locally advanced breast cancer (LABC). Treatment consists of weekly paclitaxel 175 mg/m2 intravenously over 3 hours for 6 weeks, followed by a 2-week break. Doses are modified for neutropenia (absolute neutrophil count < 1,500/microL), bilirubin levels greater than 1.5 times normal, or greater than grade 1 neuropathy. Patients with MBC continue treatment until disease progression. Patients with LABC receive one to two cycles before proceeding to surgery if resectable. Thus far, 15 patients, eight with MBC and seven with LABC, are assessable for response and/or toxicity. Most patients have required dose modification, with median delivery of 75% (cycle 1) and 50% (cycle 2) of the planned dose of paclitaxel. Neutropenia has been the most common cause of dose reductions, although only one patient required treatment for neutropenic fever. Six patients have developed grade 2/3 peripheral sensory neuropathy, but with dose reductions many have continued treatment with stable or improving neurologic symptoms. Objective responses have been seen in 12 of 14 assessable patients, including six with MBC (one complete response, five partial responses) and six with LABC (two complete responses, four partial responses), for an overall response rate of 86% (95% confidence interval, 66% to 96%). All responding LABC patients have been rendered free from disease at surgery. These preliminary results are very encouraging. Accrual to the study continues.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".