Postoperative Complications of Upfront Ovarian Cancer Surgery and Their Effects on Chemotherapy Delay
Bibliographic record
Abstract
Background: Extensive surgery on advanced-stage epithelial ovarian cancer is associated with increased postoperative morbidity, which may cause a delay in or omission of chemotherapy. We examined postoperative complications and their effects on adjuvant treatment in patients undergoing primary debulking surgery (PDS). Methods: Stage IIIC-IV epithelial ovarian cancer patients who underwent PDS between January 2013 and December 2020 were included. Patients were divided into two groups according to the radicality of the operation, i.e., extensive or standard surgery, and their outcomes were compared. Results: In total, 172 patients were included; 119 underwent extensive surgery, and 53 had standard surgery. Clavien–Dindo grade 3–5 (CDC 3+) complications were detected in 41.2% of patients after extensive operations and in 17% after standard surgery (p = 0.002). The most common CDC 3+ complication was pleural effusion. Despite the difference in the complication rates, the delay in chemotherapy did not differ between the extensive and standard groups (p = 0.98). Conclusions: Complications are common after PDS. Extensive surgery increases the complication rate, but most complications can be treated effectively; therefore, a delay in adjuvant treatment is rare.
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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 | 0.005 |
| 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) | 0.001 | 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".