Risk factors for delirium during anesthesia recovery: A meta-analysis
Bibliographic record
Abstract
Objectives: Although several risk factors for delirium during recovery from anesthesia have been identified, many risk factors remain unknown. The present study aimed to identify risk factors for delirium during recovery from anesthesia by meta-analysis. Methods: A systematic literature search of PubMed and Web of Science databases was conducted from inception until October 2021 without language restriction. All studies assessing the risk factors for delirium during recovery from anesthesia were reviewed, and the Newcastle–Ottawa Scale was used to assess the quality of included studies. Data were pooled and a meta-analysis was completed using RevMan 5.4. Results: A total of 21750 patients from 19 cohort studies were analyzed. Male gender, high American Society of Anesthesiologists (ASA) classification, and longer operation time were identified as risk factors for delirium. However, a trend for increased delirium risk was observed for high body mass index(BMI), smoking, alcohol abuse, hypertension, and longer anesthesia time, but these did not reach statistical significance. Summary: Meta analysis results showed that male gender, high ASA classification, and longer operative time were risk factors for delirium. The evidence quality in this meta-analysis was moderate, according to NOS.
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.068 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".