Prediction of postoperative mortality in older surgical patients by clinical frailty scale: A systematic review and meta-analysis
Bibliographic record
Abstract
• The prediction of frailty on postoperative adverse outcomes has been confirmed, but there are various assessment tools for frailty. This study only studied the prediction of clinical frailty scale on postoperative adverse outcomes. At the same time, we further analyzed different death time, literature types, surgical types, etc., to verify the effect of clinical frailty scale on postoperative mortality. To systematically evaluate the predictive efficacy of clinical frailty scale (CFS) for postoperative mortality older surgical patients, and to evaluate the prevalence of frailty in the included studies. A systematic review and meta-analysis of observational studies was conducted, utilizing the MOOSE guidelines for the evaluation of both. Quality assessment of the articles was also performed. The protocol was registered (CRD42023423552). Relevant English and Chinese language studies published until October 20th, 2023 were retrieved from PubMed, Web of Science, Embase, Medline, CINAHL,Cochrane, WAN FANG DATA, VIP Information, CNKI, and SinoMed databases. Study were included in which frailty was measured by the CFS and postoperative mortality was reported for older surgery patients. A meta-analysis to predict postoperative mortality and frailty prevalence was performed using STATA 17.0 software. Sixteen cohort studies were included (5,864 participants) from 1,513 records. All studies' Newcastle-Ottawa Scale (NOS) scores were above 6 points. It was found that the prevalence of surgical frailty in the older was 0.36(CI 0.20–0.52). Patients assessed as frail by the CFS were associated with higher all-cause mortality (OR:4.01; CI 2.59–6.23). Subgroup analysis shows that frailty was associated with1-month mortality (OR:3.85; CI 1.11–13.45) and 1-year mortality (OR:4.43; CI 2.18–8.99). The prevalence of frailty is high in older surgical patients, and CFS can effectively predict the mortality of older surgical patients with frailty.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.034 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".