The associations of post-stroke delirium with outcomes: a systematic review and meta-analysis
Bibliographic record
Abstract
Published data on whether post-stroke delirium (PSD) is an independent predictor of outcomes in patients with acute stroke are inconsistent and have not yet been synthesized and quantified via meta-analyses. This systematic review and meta-analysis followed the Meta-analysis of Observational Studies in Epidemiology (MOOSE) and Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. The study protocol involved a search of the PubMed, Embase, PsycINFO, and Medline databases from 1946 to November 1, 2023, of which prospective observational and case–control studies were included. The quality of the included studies was rated using the Newcastle Ottawa Scale. Pooled effect estimates calculated using a random-effects model were expressed as the odds ratios (ORs), hazard ratios (HRs), and standardized mean differences (SMDs) with 95% confidence intervals (CIs). The protocol was registered in PROSPERO (CRD42023472551). The search yielded 39 eligible articles comprising 3295 and 9643 patients with and without PSD, respectively. Thirty studies were high quality, while 9 had moderate quality. The primary analyses, adequately adjusting for predefined confounders, showed that PSD was significantly associated with mortality risk (average follow-up of 19.50 months; OR, 3.47; 95% CI, 2.35–5.12; I 2 , 26.0%) and poor neurological function (average follow-up of 21.75 months; OR, 3.62; 95% CI, 2.15–6.09; I 2 , 0). Secondary analyses, with or without inadequate adjustment, showed that PSD was significantly associated with prolonged hospital length of stay, increased risk of institutionalization, poor cognitive outcomes, and quality of life after discharge. This systematic review and meta-analysis provides evidence that PSD was independently associated with mortality and poor neurological function after controlling for pre-specified confounders. The prevention of PSD remains a high clinical and research priority.
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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 | 0.023 | 0.045 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.050 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 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".