Partial and no recovery from delirium after hospital discharge predict increased adverse events
Bibliographic record
Abstract
Background: The implications of partial and no recovery from delirium after hospital discharge are not clear. We sought to explore whether partial and no recovery from delirium among recently discharged patients predicted increased adverse events (emergency room visits, hospitalisations, death) during the subsequent 3 months. Method: Prospective study of recovery from delirium in older hospital inpatients. The Confusion Assessment Method was used to diagnose delirium in hospital and determine recovery status after discharge (T0). Adverse events were determined during the 3 months T0. Survival analysis to the first adverse event and counting process modelling for one or more adverse events were used to examine associations between recovery status (ordinal variable, 0, 1 or 2 for full, partial or no recovery, respectively) and adverse events. Results: Of 278 hospital inpatients with delirium, 172 were discharged before the assessment of recovery status (T0). Delirium recovery status at T0 was determined for 152: 25 had full recovery, 32 had partial recovery and 95 had no recovery. Forty-four patients had at least one adverse event during the subsequent 3 months. In multivariable analysis of one or more adverse events, poorer recovery status predicted increased adverse events; the hazard ratio (HR) (95% confidence interval, CI) was 1.72 (1.09, 2.71). The association of recovery status with adverse events was stronger among patients without dementia. Conclusion: Partial and no recovery from delirium after hospital discharge appear to predict increased adverse events during the subsequent 3 months These findings have potentially important implications for in-hospital and post-discharge management and policy.
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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 | 0.002 | 0.015 |
| 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".