Wanna Get Away? Maintenance Treatments and Chemotherapy Holidays in Gynecologic Cancers
Bibliographic record
Abstract
Epithelial ovarian cancer has a very high rate of relapse after primary therapy; historically approximately 70% of patients with a complete clinical response to surgery and adjuvant chemotherapy will relapse and die of the disease. Although this number has slowly improved, cure rates remain less than 50%. As such, maintenance therapy with the aim of preventing or delaying disease relapse and the goal of improving overall survival has been the subject of intense study. Numerous earlier studies with agents ranging from radioactive phosphorus to extended frontline therapy or to monthly taxol administration demonstrated encouraging improvements in progression-free survival (PFS) only to find, disappointingly, no benefit in overall survival. In addition, the PFS advantage of maintenance therapy was associated with disconcerting side effects such that maintenance therapy was not adapted as standard of care. Studies with bevacizumab and PARP inhibitors have demonstrated a PFS advantage with a manageable side-effect profile. However, an overall survival advantage remains unclear, and the use of these approaches thus remains controversial. Furthermore, in recurrent disease, the length of chemotherapy and benefits of extended chemotherapy is unclear. Thus, additional trials assessing maintenance strategies in ovarian and other gynecologic malignancies are needed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.003 | 0.001 |
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".