Interventions to improve the outcomes of frail people having surgery: A systematic review
Bibliographic record
Abstract
BACKGROUND: Frailty is an important prognostic factor for adverse outcomes and increased resource use in the growing population of older surgical patients. We identified and appraised studies that tested interventions in populations of frail surgical patients to improve perioperative outcomes. METHODS: We systematically searched Cochrane, CINAHL, EMBASE and Medline to identify studies that tested interventions in populations of frail patients having surgery. All phases of study selection, data extraction, and risk of bias assessment were done in duplicate. Results were synthesized qualitatively per a prespecified protocol (CRD42016039909). RESULTS: We identified 2 593 titles; 11 were included for final analysis, representing 1 668 participants in orthopedic, general, cardiac, and mixed surgical populations. Only one study was multicenter and risk of bias was moderate to high in all studies. Interventions were applied pre- and postoperatively, and included exercise therapy (n = 4), multicomponent geriatric care protocols (n = 5), and blood transfusion triggers (n = 1); no specific surgical techniques were compared. Exercise therapy, applied pre-, or post-operatively, was associated with significant improvements in functional outcomes and improved quality of life. Multicomponent protocols suffered from poor compliance and difficulties in implementation. Transfusion triggers had no significant impact on mortality or other outcomes. CONCLUSIONS: Despite a growing literature that demonstrates strong independent associations between frailty and adverse outcomes, few interventions have been tested to improve the outcomes of frail surgical patients, and most available studies are at substantial risk of bias. Multicenter, low risk of bias, studies of perioperative exercise are needed, while substantial efforts are required to develop and test other interventions to improve the outcomes of frail people having surgery.
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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 | 0.009 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".