The Relation Between Post-Operative Surgical Site Infection and Time to Start Adjuvant Treatment in Ovarian and Uterine Cancers
Bibliographic record
Abstract
Surgical site infections (SSIs) occur in 10–15% of patients and are linked to up to 29% of delays in starting adjuvant treatment. This study assessed the association between SSIs in patients with ovarian and uterine cancer and their impact on time to adjuvant therapy and oncologic outcomes. Patients who underwent surgery from 1 January 2015 to 30 September 2017 were included, using institutional National Surgical Quality Improvement Program (NSQIP) data and chart reviews. Among 371 patients (median follow-up 4.1 years), 243 (65.5%) received adjuvant treatment. The median time to start was 39 days for chemotherapy, 61 days for radiotherapy, and 42 days for combined therapy (p < 0.001). Patients with ovarian cancer began treatment sooner than those with uterine cancer (39 vs. 52 days, p < 0.001), but no significant difference was observed between those with or without SSIs. In 238 patients with uterine cancer, those with SSIs had a twofold higher recurrence risk (HR 1.97, p = 0.022) and over threefold lower overall survival (HR 3.45, p = 0.018). Multivariable analysis showed that surgical route and disease stage were independent predictors; SSI was not an independent factor. No survival difference related to SSIs was found in patients with ovarian cancer. Further research is needed to clarify the impact of SSIs on treatment timing and recurrence.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".