Examining the utility and accuracy of the interRAI Emergency Department Screener in identifying high‐risk older emergency department patients: A Canadian multiprovince prospective cohort study
Bibliographic record
Abstract
Objectives: We set out to determine the accuracy of the interRAI Emergency Department (ED) Screener in predicting the need for detailed geriatric assessment in the ED. Our secondary objective was to determine the discriminative ability of the interRAI ED Screener for predicting the odds of discharge home and extended ED length of stay (>24 hours). Methods: We conducted a multiprovince prospective cohort study in Canada. The need for detailed geriatric assessment was determined using the interRAI ED Screener and the interRAI ED Contact Assessment as the reference standard. A score of ≥5 was used to classify high-risk patients. Assessments were conducted by emergency and research nurses. We calculated the sensitivity, positive predictive value, and false discovery rate of the interRAI ED Screener. We employed logistic regression to predict ED outcomes while adjusting for age, sex, academic status, and the province of care. Results: A total of 5629 older ED patients across 11 ED sites were evaluated using the interRAI ED Screener and 1061 were evaluated with the interRAI ED Contact Assessment. Approximately one-third of patients were discharged home or experienced an extended ED length of stay. The interRAI ED Screener had a sensitivity of 93%, a positive predictive value of 82%, and a false discovery rate of 18%. The interRAI ED Screener predicted discharge home and extended ED length of stay with fair accuracy. Conclusion: The interRAI ED Screener is able to accurately and rapidly identify individuals with medical complexity. The interRAI ED Screener predicts patient-important health outcomes in older ED patients, highlighting its value for vulnerability screening.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".