Breast cancer clinical trials: current issues and possible solutions
Bibliographic record
Abstract
George Dranitsaris speaks to Alisa Crisp, Commissioning Editor: George Dranitsaris is a health services research consultant with graduate training in biostatistics, pharmacoeconomics, decision analysis and clinical epidemiology. His areas of interest include comparative effectiveness analysis, the measurement of cost-effective drug use in the oncology setting, value-based drug pricing and the evaluation of drug performance outside the trial setting. He has over 100 publications in the national and international literature, is past President of the Canadian Association of Pharmacy in Oncology, a statistical reviewer for the Journal of Clinical Oncology and a member of the editorial board of the Journal of Oncology Pharmacy Practice and the European Journal of Hospital Pharmacy Science.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.263 | 0.458 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.021 | 0.036 |
| Open science | 0.009 | 0.009 |
| Research integrity | 0.044 | 0.036 |
| Insufficient payload (model declined to judge) | 0.026 | 0.006 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".