Prevalence and factors influencing preoperative frailty in elderly patients with gynecologic oncology surgery: A cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Frailty is an important predictor of poor postoperative outcomes in elderly patients with gynaecologic cancer. However, the prevalence and risk factors for frailty in this population remain unclear. METHODS: This cross-sectional study was conducted simultaneously in three gynecology departments of a tertiary hospital in China between January and March 2024. The study recruited 126 hospitalised patients with gynaecologic malignancies who underwent surgery. The demographic and clinical characteristics and biochemical laboratory parameters of all patients were collected. The Edmonton Frailty Scale was used to assess the patient's frailty. Multivariate logistic regression model analysis was used to identify the influencing factors of frailty. RESULTS: The prevalence of preoperative frailty was 31 %. Univariate analysis showed significant differences between frail and non-frail groups in terms of age, body mass index, menopausal status, self-management ability, nutritional risk and activities of daily living (ADL) (all p < 0.05). Multiple logistic regression analysis identified older age (odds ratio [OR] = 1.27, 95%CI: 1.068-1.511, p = 0.007), ADL disability (OR = 3.184, 95%CI: 2.294-4.833, p = 0.010) and high nutritional risk (Nutritional Risk Screening 2002 score ≥ 3) (OR = 4.823, 95%CI: 1.422-16.816, p = 0.031) as risk factors for frailty. High self-management ability (OR = 0.918, 95%CI: 0.844-0.998, p = 0.046) was a protective factor against frailty. CONCLUSION: Nutritional support, activity exercise and improvement of patient self-management are potential intervention goals, and nurses should develop targeted prevention strategies based on identified risk factors to protect patient health.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".