“Emergency Room Evaluation and Recommendations” and Incident Hospital Admissions in Older People with Major Neurocognitive Disorders Visiting Emergency Department: Results of an Experimental Study
Bibliographic record
Abstract
INTRODUCTION: Older people with major neurocognitive disorders (MNCDs) visiting the emergency department (ED) are at high risk of hospital admissions. The "Emergency Room Evaluation and Recommendations" (ER2) tool decreases the length of stay (LOS) in the hospital when older people visiting ED are hospitalized after an index ED visit, regardless of their cognitive status. Its effect on hospital admissions has not yet been examined in older people with MNCD visiting ED. This study aimed to examine whether ER2 recommendations were associated with incident hospital admissions and LOS in ED in older people with MNCD visiting ED. METHODS: A total of 356 older people with MNCD visiting ED of the Jewish General Hospital (Montreal, Quebec, Canada) were recruited in this non-randomized, pre-post-intervention, single arm, prospective and longitudinal open label trial. ED staff and patients were blinded of the ER2 score, and patients received usual ED care during the observation period, whereas ED staff were informed about the ER2 score, and patients had ER2 tailor-made recommendations in addition to usual care during the intervention period. Hospital admissions and the LOS in ED were the outcomes. RESULTS: There were less incident hospital admissions (odds ratio ≤ 0.61 with p ≤ 0.022) and longer LOS in ED (coefficient beta ≥4.28 with p ≤ 0.008) during the intervention period compared to the observation period. DISCUSSION/CONCLUSION: ER2 recommendations have mixed effects in people with MNCD visiting ED. They were associated with reduced incident hospital admissions and increased LOS in ED, suggesting that they may have benefits in addition to usual ED care.
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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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".