Association of frailty with perioperative and survival outcomes in patients with gynecologic cancers: A population-based study
Bibliographic record
Abstract
OBJECTIVES: The relationship between frailty and both short- and long-term outcomes remains underexplored in gynecologic oncology (GO). We sought to evaluate the association of frailty with 30-day complications, costs and mortality, and long-term survival following surgery. METHODS: A population-based observational retrospective cohort study of patients undergoing a laparotomy for a gynecologic malignancy between 2009 and 2021 was conducted using Ontario province-wide databases. Frailty was defined using the preoperative frailty index (pFI) and the John Hopkins Adjusted Clinical Groups frailty indicator (ACG). RESULTS: Among 21,359 patients, 1405 (6.6 %) and 1144 (5.4 %) were frail using the pFI and ACG. Frailty as assessed by the pFI was associated with an increased risk of 30-daycomplications (25.7 % vs 7.4 %, p < 0.0001), 30-day mortality (2.9 % vs 0.5 %, p < 0.0001), 90-day mortality (7.1 % vs 1.4 %, p < 0.0001), 30-day mean healthcare costs ($16,478 vs $9306, p < 0.0001), and lower median 5 year-survival (3.28 years versus not reached). Frailty was independently associated with 30-day complications (OR 1.92, 95 % CI 1.63-2.27, p < 0.0001)) in multivariable regression analysis adjusting for age, income quintile, primary cancer, stage, type of surgery and neoadjuvant chemotherapy, and with lower 5-year survival (HR 1.27, 95 % CI 1.16-1.38, p < 0.0001) adjusting for age, primary cancer, stage, neoadjuvant chemotherapy and comorbidities. Results for these outcomes were similar using the ACG. The ROC analysis revealed similar area under the curve for both indices. CONCLUSIONS: Frailty as measured by both the pFI and ACG was predictive of outcomes including increased postoperative morbidity and mortality, and 5-year survival. Strategies to optimize perioperative care for frailty are required.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".