Understanding preoperative concerns and attitudes towards prehabilitation in older surgical populations: A survey study
Bibliographic record
Abstract
STUDY OBJECTIVES: To identify common preoperative concerns in older surgical populations and explore their attitudes towards prehabilitation, functional, and cognitive assessments. DESIGN: Multicenter cross-sectional study. SETTING: Preoperative questionnaire examining preoperative concerns and attitudes towards prehabilitation, functional, and cognitive assessments. PATIENTS: 236 non-cardiac surgical patients ≥65 years old. MEASUREMENTS: Concerns across five domains-surgery, anesthesia, functional status, cognitive status, and financial burden-were measured on a five-point Likert scale, ranging from 'not concerned at all' to 'very concerned.' Attitudes towards prehabilitation and preoperative assessments were assessed on a five-point scale from 'strongly disagree' to 'strongly agree.' Exploratory factor analysis identified concerns, and confirmatory factor analysis validated them. Reliability was assessed with Cronbach's alpha, and model fit was evaluated using the root mean square error of approximation, comparative fit index, and related indices. RESULTS: Surgical concerns were highest (2.5 ± 1.2), particularly regarding postoperative pain, surgical failure, and complications. Concerns about anesthesia (2.0 ± 1.3) and functional status (1.9 ± 1.3) followed, with lower concerns about cognitive status (1.5 ± 1.1) and financial burden (1.4 ± 0.9). Attitudes towards prehabilitation were generally positive. Most participants were open to functional assessments and training programs, though only 37 % were willing to undergo memory assessment. Factor analysis revealed a five-factor structure of preoperative concerns: basic activities of daily living, instrumental activities of daily living, surgical/anesthesia concerns, cognitive/financial concerns, and discharge concerns. Confirmatory factor analysis supported this structure with adequate model fit. CONCLUSIONS: This study highlights common preoperative concerns among older adults, particularly regarding surgery, anesthesia, and functional status, with greater concern reported by those with functional dependence. While attitudes towards prehabilitation were generally positive, there was low willingness to undergo preoperative memory testing. Future research should refine prehabilitation programs to optimize effectiveness, accessibility, and adherence in older adults and further evaluate their impact on surgical outcomes.
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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 | 0.006 | 0.003 |
| 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".