Frailty factors and outcomes in vascular surgery patients: a systematic review and meta-analysis
Bibliographic record
Abstract
Objective <div>To describe and critique tools used to assess frailty in vascular surgery patients, and\ninvestigate its associations with patient factors and outcomes. </div><div>Background </div><div>Increasing evidence shows negative impacts of frailty on outcomes in surgical\npatients, but little investigation of its associations with patient factors has been\nundertaken.\nMethods </div><div>Systematic review and meta-analysis of studies reporting frailty in vascular surgery\npatients (PROSPERO registration: CRD42018116253) searching Medline, Embase,\nCINAHL, PsycINFO and Scopus. Quality of studies was assessed using Newcastle Ottawa scores (NOS) and quality of evidence using GRADE criteria. Associations of\nfrailty with patient factors were investigated by difference in means (MD) or\nexpressed as risk ratios (RR), and associations with outcomes expressed as odds\nratios (OR) or hazard ratios (HR). Data were pooled using random effects models.\nResults </div><div>Fifty-three studies were included in the review and only 8 (15%) were both good\nquality (NOS ≥7) and used a well-validated frailty measure. Eighteen studies (62,976\npatients) provided data for the meta-analysis. Frailty was associated with increased\nage (MD 4.05 years; 95% confidence interval [CI] 3.35, 4.75), female sex (RR 1.32;\n95%CI 1.14, 1.54), and lower body-mass index (MD -1.81; 95%CI -2.94, -0.68).\nFrailty was associated with 30-day mortality (adjusted [A]OR 2.77; 95%CI 2.01-3.81), \npost-operative complications (AOR 2.16; 95%CI 1.55, 3.02) and long-term mortality\n(HR 1.85; 95%CI 1.31, 2.62). Sarcopenia was not associated with any outcomes. </div><div>Conclusion </div><div>Frailty, but not sarcopenia, is associated with worse outcomes in vascular surgery\npatients. Well-validated frailty assessment tools should be preferred clinically, and in\nfuture research.</div>
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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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.136 | 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".