Frailty as a Predictor of Death or New Disability After Surgery
Bibliographic record
Abstract
OBJECTIVE: To compare the accuracy of the modified Fried Index (mFI) and the Clinical Frailty Scale (CFS) to predict death or patient-reported new disability 90 days after major elective surgery. BACKGROUND: The association of frailty with patient-reported outcomes, and comparisons between preoperative frailty instruments are poorly described. METHODS: This was a prospective multicenter cohort study. We determined frailty status in individuals ≥65 years having elective noncardiac surgery using the mFI and CFS. Outcomes included death or patient-reported new disability (primary); safety incidents, length of stay (LOS), and institutional discharge (secondary); ease of use, usefulness, benefit, clinical importance, and feasibility (tertiary). We measured the adjusted association of frailty with outcomes using regression analysis and compared true positive and false positive rates (TPR/FPR). RESULTS: Of 702 participants, 645 had complete follow up. The CFS identified 297 (42.3%) with frailty, the mFI 257 (36.6%); 72 (11.1%) died or experienced a new disability. Frailty was significantly associated with the primary outcome (CFS adjusted odds ratio, OR, 2.51, 95% confidence interval, CI, 1.50-4.21; mFI adjusted-OR 2.60, 95% CI 1.57-4.31). TPR and FPR were not significantly different between instruments. Frailty was the only significant predictor of death or new disability in a multivariable analysis. Need for institutional discharge, costs and LOS were significantly increased in individuals with frailty. The CFS was easier to use, required less time and had less missing data. CONCLUSIONS: Older people with frailty are significantly more likely to die or experience a new patient-reported disability after surgery. Clinicians performing frailty assessments before surgery should consider the CFS over the mFI as accuracy was similar, but ease of use and feasibility were higher.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".