Risk factors and outcomes among delirium subtypes in adult ICUs: A systematic review
Bibliographic record
Abstract
PURPOSE: Use systematic review methodology to summarize risk factors and outcomes for each delirium subtype (hypoactive, hyperactive and mixed) in an adult ICU population. MATERIALS AND METHODS: We searched the MEDLINE, Embase, CINAHL, SCOPUS, Web of Science and PsycINFO databases from database inception until August 13, 2018, with no restrictions. RESULTS: Of 9635 abstracts, 20 studies were included. Older age was not associated with any delirium subtype in 4/7 (57%) studies. Sex was not associated with any delirium subtype in 4/4 (100%) studies. Mortality was consistently associated with hypoactive delirium in 4/7 (57%) studies. The evidence supporting the association of APACHE-II score, mechanical ventilation, length of stay, duration of delirium and removal of tubes were inconsistent across studies. CONCLUSIONS: Although included studies reported on many subtype-specific risk factors and outcomes, heterogeneity in reporting and methodological quality limited the generalizability of the results and the evidence for many subtype-specific risk factors or outcomes is inconsistent across studies. Standardized methodology and the creation of a universal template for collecting data in ICU delirium studies are essential moving forward; helping to identify subtype-specific risk factors or outcomes and strengthen the association of potential risk factors or outcomes.
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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 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".