Incidence and associated factors of delirium after primary total joint arthroplasty in elderly patients: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: A total of 1.5% to 20.2% of total joint arthroplasty patients experience delirium. Until now, no formal systematic review or meta-analysis was performed to summarize the risk factors of delirium after primary total joint arthroplasty (TJA). METHODS: A comprehensive search encompassing Medline, Embase, and the Cochrane central database was conducted, incorporating studies available up to June 2023. We systematically reviewed research on the risk factors contributing to delirium following TJA in elderly patients, without language restrictions. The methodological quality of the included studies was evaluated using the Newcastle-Ottawa Scale. Data synthesis through pooling and a meta-analysis were performed to analyze the findings. RESULTS: A total of 23 studies altogether included 71,095 patients with primary TJA, 2142 cases of delirium occurred after surgery, suggesting the accumulated incidence of 3.0%. The results indicated that age, current smoker, heavy drinker, mini-mental state examination score, hypertension, diabetes mellitus, chronic kidney disease, history of stroke, coronary arterial disease, dementia, history of psychiatric illness, American Society of Anesthesiologists physical status III-IV, general anesthesia, anesthesia time, operative time, intraoperative blood loss, blood transfusion, β-blockers, ACEI drugs, use of psychotropic drugs, preoperative C-reactive protein level, and preoperative albumin level were significantly associated with postoperative delirium after primary TJA. CONCLUSIONS: Related prophylaxis strategies should be implemented in the elderly involved with above-mentioned risk factors to prevent delirium after primary TJA.
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.037 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".