Risk factors for postoperative delirium in geriatric patients with hip fracture: A systematic review and meta-analysis
Bibliographic record
Abstract
Objectives: This systematic review and meta-analysis was conducted to identify the potential risk factors for postoperative delirium in geriatric patients with hip fracture. Methods: PubMed, EMBASE, and Cochrane Library were searched from inception until December 31st, 2021. A combined searching strategy of subject words and free words was adopted. Studies involving risk factors for postoperative delirium in elderly patients undergoing hip fracture surgeries were reviewed. Qualities of included studies were assessed using the Newcastle-Ottawa Scale. Data were pooled and a meta-analysis was performed using Review Manager 5.3. Results: A total of 37 studies were included. The following risk factors were significant: advanced age (per year increase) (OR: 1.05, 95% CI 1.04-1.07), age>80 years (OR: 2.26, 95% CI 1.47-3.47), male (OR: 1.53, 95% CI 1.37-1.70), preoperative cognitive impairment (OR:3.20, 95% CI 2.12-4.83), preoperative dementia (OR: 2.74, 95% CI 2.18-3.45), preoperative delirium (OR: 9.23, 95% CI 8.26-10.32), diabetes (OR: 1.18, 95% CI 1.05-1.33), preoperative functional dependence (OR: 1.31, 95% CI 1.11-1.56), ASA level (per level increase) (OR: 1.63, 95% CI 1.04-2.57), ASA level≥3(OR: 1.76, 95% CI 1.39-2.24), low albumin (OR: 3.30, 95% CI 1.44-7.55), medical comorbidities (OR: 1.15, 95% CI 1.06-1.25), Parkinson's disease (OR: 4.17, 95% CI 1.68-10.31) and surgery delay>48 h (OR: 1.90, 95% CI 1.36-2.65). Conclusions: Clinicians should be alert to patients with those risk factors. To identify the risk factors more precisely, more research studies with larger sample size and better design should be conducted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.041 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".