Bibliographic record
Abstract
Zarnie Lwin MBBS FCP(SA) FRACPa & Natasha Leighl MD MMSc FRCPC*aa University of Toronto, Princess Margaret Hospital, Division of Medical Oncology and Haematology, 5-105, 610 University Avenue, Toronto, Ontario M5G 2M9, Canada, USA +1 416 946 4645; +1 416 946 6546; † Author for correspondenceBreast cancer is among the leading causes of cancer morbidity worldwide and accounts for a significant proportion of overall healthcare costs. Cost-effectiveness and cost-utility evaluations of therapy provide insight into the societal value of different treatments, helping decision-makers to prioritize resource allocation and maximize benefit in cancer control within resource constraints. Docetaxel, a plant alkaloid of the taxane group, has been recognized as a highly active chemotherapy agent in breast, lung and prostate cancers, among others. In the last decade, docetaxel has become incorporated into the neoadjuvant, adjuvant and metastatic treatment of breast cancer. This article reviews the economic data supporting the use of docetaxel in the treatment of breast cancer.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 source (direct Gemma or distilled Codex), 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".