Frailty and decisional regret after elective noncardiac surgery: a multicentre prospective cohort study
Bibliographic record
Abstract
BACKGROUND: Frailty is associated with morbidity and mortality after surgery. The association of frailty with decisional regret is poorly defined. Our objective was to estimate the association of preoperative frailty with decisional regret status in the year after surgery. METHODS: We conducted a secondary analysis of a prospective, multicentre cohort study of patients aged ≥65 years who underwent elective noncardiac surgery. Decisional regret about having undergone surgery was ascertained at 30, 90, and 365 (primary time point) days after surgery using a 3-point ordinal scale. Bayesian ordinal logistic regression was used to estimate the association of frailty with decisional regret, adjusted for surgery type, age, sex, and mental health conditions. Subgroup and sensitivity analyses were conducted. RESULTS: We identified 669 patients; 293 (43.8%) lived with frailty. At 365 days after surgery, the unadjusted odds ratio (OR) associating frailty with greater decisional regret was 2.21 (95% credible interval [CrI] 0.98-5.09; P(OR>1)=0.97), which was attenuated after confounder adjustment (adjusted OR 1.68, 95% CrI 0.84-3.36; P(OR>1)=0.93). Similar results were estimated at 30 and 90 days. Additional adjustment for baseline comorbidities and disability score substantially altered the OR at 365 days (0.89, 95% CrI 0.37-2.12; P(OR>1)=0.39). There was a high probability that surgery type was an effect modifier (non-orthopaedic: OR 1.90, 95% CrI 1.00-3.59; P(OR>1)=0.98); orthopaedic: OR 0.87, 95% CrI 0.41-1.91; P(OR>1)=0.36). CONCLUSIONS: Among older surgical patients, there appears to be a complex association with frailty and decisional regret, with substantial heterogeneity based on assumed causal pathways and surgery type. Future studies are required to untangle the complex interplay between these factors.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".