Delirium Predicts 12-Month Mortality
Bibliographic record
Abstract
BACKGROUND: Delirium has not been found to be a significant predictor of postdischarge mortality, but previous research has methodologic limitations including small sample sizes and inadequate control of confounding. This study aimed to determine the independent effects of presence of delirium, type of delirium (incident vs prevalent), and severity of delirium symptoms on 12-month mortality among older medical inpatients. METHODS: A prospective, observational study of 2 cohorts of medical inpatients was conducted with patients 65 years or older: 243 patients had prevalent or incident delirium, and 118 controls had no delirium. Baseline measures included presence of delirium and/or dementia, severity of delirium symptoms, physical function, comorbidity, and physiological and clinical severity of illness. Mortality during the 12 months after enrollment was analyzed with the Cox proportional hazards model with adjustment for covariates. RESULTS: The unadjusted hazard ratio of delirium with mortality was 3.44 (95% confidence interval, 2.05-5.75); the adjusted hazard ratio was 2.11 (95% confidence interval, 1.18-3.77). The effect of delirium was sustained over the entire 12-month period after adjustment for covariates and was stronger among patients without dementia. Among patients with dementia, there was a weak, nonsignificant effect of delirium on survival. After adjustment for covariates, mortality did not differ between patients with incident and prevalent delirium, but among patients with delirium without dementia, greater severity of delirium symptoms was associated with higher mortality. CONCLUSIONS: Delirium is an independent marker for increased mortality among older medical inpatients during the 12 months after hospital admission. It is a particularly important prognostic marker among patients without dementia.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".