Unraveling the impact of frailty on postoperative delirium in elderly surgical patients: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Frailty has been consistently implicated as a pivotal factor in the onset of delirium following anesthesia and surgery. Nonetheless, a comprehensive understanding of the relationship between frailty and delirium remains to be elucidated. This study addresses that knowledge gap. METHODS: A comprehensive search of literature databases identified 43 relevant studies involving 14,441 participants. The studies were subjected to a rigorous quality assessment using the Newcastle-Ottawa Scale. Statistical analysis was conducted using Review Manager (v5.4.1), including subgroup and sensitivity analyses. RESULTS: = 46.7%). The baseline frailty rate was 34.0% (4,910/14,441), while the overall incidence of postoperative delirium was 20% (2,783/14,441). Subgroup analyses based on characteristics such as race, frailty-assessment tools, and surgical types were conducted to explore potential sources of heterogeneity. This meta-analysis provided compelling evidence supporting a notable link between preoperative physical frailty and an increased risk of postoperative delirium in older surgical patients. Early identification through frailty screening can enable targeted interventions, potentially enhancing overall management and individualized treatment. Integrating frailty assessment into preoperative evaluation may improve predictive accuracy in surgical planning and anesthesia management. CONCLUSIONS: Future research could focus on optimizing the integration of frailty assessment into preoperative protocols for timely intervention and improved patient outcomes. TRIAL REGISTRATION: The review protocol was registered with PROSPERO (CRD42023390486), date of registration: Aug 11, 2023.
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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 | 0.001 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.012 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".