The impacts of neoadjuvant chemotherapy and of cytoreductive surgery on 10‐year survival from advanced ovarian cancer
Bibliographic record
Abstract
OBJECTIVE: To compare the long-term survival outcomes for women with advanced ovarian cancer treated with chemotherapy either before or after surgery (neoadjuvant chemotherapy vs primary cytoreductive surgery) at a single tertiary cancer center. METHODS: Retrospective cohort study of 326 patients with Stage IIIC or IV high-grade serous ovarian cancer who received neoadjuvant chemotherapy or primary cytoreductive surgery between 2001 and 2011. Clinical treatments were recorded and 10-year survival rates were measured. RESULTS: A total of 183 women (56.1%) underwent primary cytoreductive surgery and 143 women (43.9%) received neoadjuvant chemotherapy. Women who received neoadjuvant chemotherapy were more likely to have no residual disease than those who underwent primary cytoreductive surgery (51.4% vs 41.5%; P = 0.030) but experienced inferior 10-year overall survival (9.1% vs 19.3%; P < 0.001). Among those who had primary cytoreductive surgery, those with no residual disease had superior 10-year overall survival than those who had any evidence of residual disease (36.0% vs 7.2%; P < 0.001). CONCLUSION: Among women with advanced ovarian cancer, those who underwent primary cytoreductive surgery had better survival than those who received neoadjuvant chemotherapy. Neoadjuvant chemotherapy should be reserved for those in whom optimal primary cytoreductive surgery is not feasible.
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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 | 0.003 |
| 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".