Revisiting the Role of Radiation Treatment for Non-serous Subtypes of Epithelial Ovarian Cancer
Bibliographic record
Abstract
Except for its palliative use, radiation has been largely abandoned in the management of ovarian cancers because of the recognized efficacy of chemotherapy agents. Whole abdominal irradiation (WAR), however, has been shown to be of adjuvant and curative value in ovarian cancer with microscopic or minimal residual disease in the pelvis, the so-called “intermediate risk group.” Recent hypothesis generating data from the use of adjuvant radiation following adjuvant chemotherapy in ovarian cancer has shown an incremental survival benefit for the rarer non-serous ovarian subtypes including clear cell, endometrioid, and mucinous. No incremental benefit was observed for the more common serous subtype. A retrospective examination of early trials using WAR as the sole postoperative treatment in ovarian cancer has determined that the majority of patients in these studies and cured by radiation actually had the non-serous subtypes. The recognition that the non-serous subtypes differ from the serous cancers in their stage of presentation, their molecular characteristics, their response to classic chemotherapy, and their outcomes suggest the non-serous subtypes should be treated as rare and different cancers. In addition to specific targeting therapies that may be developed, radiation should be reconsidered as part of the treatment armamentarium for these diseases.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".