Another Confirmation of the Inactivity of Topotecan in Ovarian Cancer
Bibliographic record
Abstract
One decade ago, in an editorial in the Journal ( 1 ), I challenged investigators who were interested in improving outcomes to discontinue conducting multiple trials that addressed similar questions in the treatment of advanced ovarian carcinoma except when the purpose of such studies was to confirm possible practice-changing new treatment strategies. A negative report that appears in this issue ( 2 ) is the fifth study to investigate the addition of topotecan to the platinum and taxane backbone, the current standard of care for ovarian cancer that was confirmed here a decade ago ( 3 ). The authors even include a table in the article that demonstrates the accrual of 4451 patients to explore various permutations of the question: Does the addition of topotecan to a platinum and taxane backbone improve outcomes in patients with advanced ovarian carcinoma? The answer is a resounding “no” whether topotecan is used in a triplet ( 4 ), as part of a sequential doublet ( 2 , 5 ), or as consolidation ( 6 , 7 ).
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 | 0.014 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.008 | 0.020 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".