Accuracy and Feasibility of Clinically Applied Frailty Instruments before Surgery
Bibliographic record
Abstract
BACKGROUND: A barrier to routine preoperative frailty assessment is the large number of frailty instruments described. Previous systematic reviews estimate the association of frailty with outcomes, but none have evaluated outcomes at the individual instrument level or specific to clinical assessment of frailty, which must combine accuracy with feasibility to support clinical practice. METHODS: The authors conducted a preregistered systematic review (CRD42019107551) of studies prospectively applying a frailty instrument in a clinical setting before surgery. Medline, Excerpta Medica Database, Cochrane Library and the Comprehensive Index to Nursing and Allied Health Literature, and Cochrane databases were searched using a peer-reviewed strategy. All stages of the review were completed in duplicate. The primary outcome was mortality and secondary outcomes reflected routinely collected and patient-centered measures; feasibility measures were also collected. Effect estimates were pooled using random-effects models or narratively synthesized. Risk of bias was assessed. RESULTS: Seventy studies were included; 45 contributed to meta-analyses. Frailty was defined using 35 different instruments; five were meta-analyzed, with the Fried Phenotype having the largest number of studies. Most strongly associated with: mortality and nonfavorable discharge was the Clinical Frailty Scale (odds ratio, 4.89; 95% CI, 1.83 to 13.05 and odds ratio, 6.31; 95% CI, 4.00 to 9.94, respectively); complications was associated with the Edmonton Frail Scale (odds ratio, 2.93; 95% CI, 1.52 to 5.65); and delirium was associated with the Frailty Phenotype (odds ratio, 3.79; 95% CI, 1.75 to 8.22). The Clinical Frailty Scale had the highest reported measures of feasibility. CONCLUSIONS: Clinicians should consider accuracy and feasibility when choosing a frailty instrument. Strong evidence in both domains support the Clinical Frailty Scale, while the Fried Phenotype may require a trade-off of accuracy with lower feasibility.
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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 | 0.178 | 0.528 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.014 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".