Emergency Department Stay Associated Delirium in Older Patients*
Bibliographic record
Abstract
BackgroundCaring for older patients can be challenging in the Emergency Department (ED). A > 12 hr ED stay could lead to incident episodes of delirium in those patients. The aim of this study was to assess the incidence and impacts of EDstay associated delirium.MethodsA historical cohort of patients who presented to a Canadian ED in 2009 and 2011 was randomly constituted. Included patients were aged ≥ 65 years old, admitted to any hospital ward, non-delirious upon arrival and had at least a 12-hour ED stay. Delirium was detected using a modified chart-based Confusion Assessment Method (CAM) tool. Hospital lengthof stay (LOS) was log-transformed and linear regression assessed differences between groups. Adjustments were madefor age and comorbidity profile.Results200 records were reviewed, 55.5% were female, median age was 78.9 yrs (SD:7.3). 36(18%) patients experienced ED-stay associated delirium. Nearly 50% of episodes started in the ED and within 36 hours of arrival. Comorbidity profile was similar between the positive CAM group and the negative CAM group. Mean adjusted hospital LOS were 20.5 daysand 11.9 days respectively (p<.03).Conclusions1 older adult out of 5 became delirious after a 12 hr ED stay. Since delirium increases hospital LOS by more than a week, better screening and implementation of preventing measures for delirium could reduce LOS and overcrowding in the ED.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".