Frailty in middle-aged and older adult postoperative patients with gynecological malignancies structural equation modeling
Bibliographic record
Abstract
Background: Frailty and self-management are important determinants of quality of life in cancer patients. However, their synergistic effects and potential mechanisms on quality of life in middle-aged and older adult postoperative gynecologic malignancy patients have not been adequately studied. Objective: This cross-sectional study aimed to explore the relationship between frailty, self-management, and quality of life in middle-aged and older adult postoperative gynecologic malignancy patients. Methods: A cross-sectional study was conducted from January 2024 to April 2024 in three gynecological wards of a tertiary hospital in Wuxi. The study recruited 177 patients aged 45 years or older who underwent surgery for gynecologic malignancies (cervical, ovarian, and endometrial cancer). Data were collected using demographic and clinical characteristics, the Edmonton Frailty Scale, the Self-Management Competence Scale, and the EORTC Core Quality of Life Questionnaire. Structural equation modeling was used to explore the interactions between frailty, self-management, and quality of life. Results: The prevalence of frailty in middle-aged and older adult postoperative gynecologic malignancy patients was 39.5%, with a mean total self-management score of 125.81 ± 13.21 and a mean total quality of life score of 69.26 ± 10.88. The fit indices of the model indicated a good fit, and that frailty had multiple effects on quality of life; specifically, frailty could affect the quality of life directly or through self-management, i.e., self-management partially mediated frailty and quality of life. Conclusion: Self-management is a mediating variable between frailty and quality of life, suggesting that clinical workers can intervene in self-management skills to improve patient's quality of life and physical and mental health.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".