Prevalence and risk factors of subsyndromal delirium among postoperative patients: A systematic review and meta‐analysis
Bibliographic record
Abstract
AIM: The aim of this study is to determine the prevalence and risk factors for subsyndromal delirium in the postoperative patient. DESIGN: A systematic review and meta-analysis. METHODS: The Review Manager 5.3 statistics platform and the Newcastle-Ottawa Scale were used for quality evaluation. DATA SOURCES: The following databases were searched: PubMed, Web of Science, EMBASE, The Cochrane Library, Scopus and EBSCO from January 2000 to December 2021. Additional sources were found by looking at relevant articles' citations. RESULTS: A total of 1744 titles were originally identified, and five studies including 962 patients were included in the systematic review, with a pooled prevalence of postoperative subsyndromal delirium (PSSD) of 30% (95% CI: 0.28-0.32). Significant risk variables for PSSD were older age, low levels of education (≤9 years), cognitive impairment, higher comorbidity score, and the duration of operation. CONCLUSION: PSSD is prevalent and is associated with a variety of risk factors as well as low academic performance. IMPACT: Identification and clinical management of patients with PSSD should be improved. Future research on PSSD risk factors should look at a wider range of intraoperative and postoperative risk factors that can be changed. PATIENT AND PUBLIC CONTRIBUTION: No Patient or Public Contribution.
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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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.031 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".