Surgical outcomes in women with ovarian cancer.
Bibliographic record
Abstract
OBJECTIVE: We sought to assess whether the specialty of the surgeon or the hospital involved in the initial management of women with ovarian cancer determined the likelihood of unnecessary repeated abdominal surgery and long-term patient survival. METHODS: We conducted a population-based study involving women in Ontario, Canada, who had epithelial ovarian cancer treated initially with abdominal surgery between January 1996 and December 1998. We documented incident surgical cases using hospital contact data and the Ontario Cancer Registry. We obtained data on patient characteristics, clinical findings, surgical techniques and perioperative care from electronic administrative data records and patient charts. We performed regression analyses to assess the influence of surgeon and hospital specialization and of case volumes on the likelihood of repeat surgery and survival. We controlled for stage of disease and other factors associated with these outcomes. We also examined the relation between the adequacy of surgery and adjuvant chemotherapy with survival. RESULTS: A total of 1341 women met our inclusion criteria. Our analysis showed that repeat surgery was associated with the surgeon's discipline, younger patient age, well-differentiated tumours and early stage of disease. However, survival was not associated with the surgeon's discipline; rather, it was associated with advanced patient age, increasing comorbidities, advanced stage of disease, poorly differentiated tumours, urgent surgery and adjuvant chemotherapy. We observed a trend between inadequate surgery and a decreased likelihood of survival. CONCLUSION: Further study is needed to understand patterns of repeat surgery for ovarian cancer. Improved quality of operative reporting is required to classify surgical adequacy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".