The consistent burden in published estimates of delirium occurrence in medical inpatients over four decades: a systematic review and meta-analysis study
Bibliographic record
Abstract
INTRODUCTION: Delirium is associated with a wide range of adverse patient safety outcomes, yet it remains consistently under-diagnosed. We undertook a systematic review of studies describing delirium in adult medical patients in secondary care. We investigated if changes in healthcare complexity were associated with trends in reported delirium over the last four decades. METHODS: We used identical criteria to a previous systematic review, only including studies using internationally accepted diagnostic criteria for delirium (the Diagnostic and Statistical Manual of Mental Disorders and the International Statistical Classification of Diseases). Estimates were pooled across studies using random effects meta-analysis, and we estimated temporal changes using meta-regression. We investigated publication bias with funnel plots. RESULTS: We identified 15 further studies to add to 18 studies from the original review. Overall delirium occurrence was 23% (95% CI 19-26%) (33 studies) though this varied according to diagnostic criteria used (highest in DSM-IV, lowest in DSM-5). There was no change from 1980 to 2019, nor was case-mix (average age of sample, proportion with dementia) different. Overall, risk of bias was moderate or low, though there was evidence of increasing publication bias over time. DISCUSSION: The incidence and prevalence of delirium in hospitals appears to be stable, though publication bias may have masked true changes. Nonetheless, delirium remains a challenging and urgent priority for clinical diagnosis and care pathways.
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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 | 0.065 | 0.148 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.044 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".