Does Delirium Increase Hospital Stay?
Bibliographic record
Abstract
OBJECTIVES: To determine the effects of prevalent and incident delirium on length of hospital stay. DESIGN: Prospective cohort study, comparing (1). length of stay after admission in cases of prevalent delirium versus controls without prevalent delirium with (2). length of stay after diagnosis in cases of incident delirium versus controls matched by day of diagnosis. SETTING: The medical services of a primary, acute care hospital. PARTICIPANTS: Medical admissions of patients aged 65 and older from the emergency department with delirium diagnosed during the first week in hospital. Patients admitted to intensive care or oncology and those with a primary diagnosis of stroke were excluded. A sample of those without delirium was also enrolled. MEASUREMENTS: Delirium was diagnosed using the Confusion Assessment Method. Data on length of stay and diagnosis-related groups (DRGs) were abstracted from administrative data. Measures of covariates included the Informant Questionnaire on Cognitive Decline in the Elderly, the Delirium Index, the instrumental activities of daily living questionnaire from the Older American Resources and Services project, the Charlson Comorbidity Index, the Clinical Severity Scale, and the Acute Physiology Score. RESULTS: The study sample comprised 359 patients: 204 with prevalent delirium, 37 with incident delirium, and 118 without delirium. After controlling for covariates, prevalent delirium was not associated with a significantly longer hospital stay, but incident delirium was associated with an excess stay after diagnosis of 7.78 days (95% confidence interval=3.07, 12.48). Similar results were obtained using log-transformed or DRG-adjusted estimates of length of stay. CONCLUSION: In older medical inpatients, incident but not prevalent delirium is an important predictor of longer hospital stay. Interventions to prevent incident delirium may reduce length of stay.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".