Impact of interval from primary cytoreductive surgery to initiation of adjuvant chemotherapy in advanced epithelial ovarian cancer
Bibliographic record
Abstract
OBJECTIVE: To determine the optimal timing of adjuvant chemotherapy after primary cytoreductive surgery for advanced epithelial ovarian cancer. METHODS: In a retrospective cohort analysis, data were assessed from women with advanced epithelial ovarian carcinoma treated at Princess Margaret Cancer Centre, Toronto, Canada between 2002 and 2012, and at Samsung Medical Centre, Seoul, Korea, between 2002 and 2015. The treatment interval was defined as the time period between primary cytoreductive surgery and the first cycle of adjuvant chemotherapy. RESULTS: Overall, 711 women met the inclusion criteria. Among them, 247 (34.7%) had optimal cytoreduction (residual 1-9 mm), 229 (32.2%) had microscopic residual disease (0 mm), and 235 (33.1%) had suboptimal cytoreduction (≥10 mm). The median time of treatment interval was 10 days (range 3-86 days). In the optimal (1-9 mm) group, a longer treatment interval was significantly associated with poor overall survival (hazard ratio 1.02, 95% confidence interval 1.01-1.03; P=0.001) in multivariate analysis. Treatment interval was not associated with a significant difference in overall survival in the microscopic or suboptimal residual disease groups. CONCLUSION: Overall survival might be negatively affected by longer treatment intervals among women with advanced epithelial ovarian carcinoma.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".