Risk factors of delirium after gastrointestinal surgery: A meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Postoperative delirium is one of the common complications after any major surgery such as gastrointestinal surgery. And it is related to increased mortality and morbidity and other serious surgical outcomes. AIMS: This study aims to identify risk factors for postoperative delirium in patients undergoing gastrointestinal surgery. MATERIALS AND METHODS: Relevant studies published before August 2021 were searched on Pubmed, Embase and Medline. The risk of bias of included studies was assessed by Newcastle-Ottawa Scale (NOS). A random-effects model of DerSimonian-Laird was used to synthesise the overall ORs or RRs for all risk factors. MOOSE checklist was used to review this manuscript. RESULTS: A total of 21 studies including 6165 patients were finally included for quantitative analysis. The pooled incidence of postoperative delirium is 11% (95% CI: 9%-15%). 16 risk factors were identified, in which age, sex, alcohol consumption, cerebrovascular diseases, cardiovascular diseases, use of sleeping pills, history of delirium, preoperative C-reactive protein (CRP) levels, operation time, blood loss and perioperative blood transfusion were statistically significant while smoking, American Society of Anesthesiologists (ASA) score, performance status, diabetes and operation approach were insignificant. DISCUSSION: This meta-analysis may provide tips for nursing staff and surgeons to design and implement prevention programmes to reduce the incidence of postoperative delirium. CONCLUSION: Potential risk factors of delirium after gastrointestinal surgery are age, sex, alcohol consumption, cerebrovascular diseases, cardiovascular diseases, use of sleeping pills, history of delirium, preoperative CRP levels, operation time, blood loss and blood transfusion.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.061 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".