“Emergency Room Evaluation and Recommendations” (ER2) Tool for the Screening of Older Emergency Department Visitors With Major Neurocognitive Disorders: Results From the ER2 Database
Bibliographic record
Abstract
Purpose: The Emergency Room Evaluation and Recommendation (ER2) is an application in the electronic medical file of patients visiting the Emergency Department (ED) of the Jewish General Hospital (JGH; Montreal, Quebec, Canada). It screens for older ED visitors at high risk of undesirable events. The aim of this study is to examine the performance criteria (i.e., sensitivity, specificity, positive predictive value [PPV], negative predictive value [NPV], positive likelihood ratio [LR+], negative likelihood ratio [LR-] and area under the receiver operating characteristic curve [AUROC]) of the ER2 high-risk level and its “temporal disorientation” item alone to screen for major neurocognitive disorders in older ED visitors at the JGH. Methods: Based on a cross-sectional design, 999 older adults (age 84.9 ± 5.6, 65.1% female) visiting the ED of the JGH were selected from the ER2 database. ER2 was completed upon the patients' arrival at the ED. The outcomes were ER2's high-risk level, the answer to ER2's temporal disorientation item (present vs. absent), and the diagnosis of major neurocognitive disorders (yes vs. no) which was confirmed when it was present in a letter or other files signed by a physician. Results: The sensitivities of both ER2's high-risk level and temporal disorientation item were high (≥0.91). Specificity, the PPV, LR+, and AROC were higher for the temporal disorientation item compared to ER2's high-risk level, whereas a highest sensitivity, LR-, and NPV were obtained with the ER2 high-risk level. Both area under the receiver operating characteristic curves were high (0.71 for ER2's high-risk level and 0.82 for ER2 temporal disorientation item). The odds ratios (OR) of ER2's high-risk level and of temporal disorientation item for the diagnosis of major neurocognitive disorders were positive and significant with all OR above 18, the highest OR being reported for the temporal disorientation item in the unadjusted model [OR = 26.4 with 95% confidence interval (CI) = 17.7–39.3]. Conclusion: Our results suggest that ER2 and especially its temporal disorientation item may be used to screen for major neurocognitive disorders in older ED users.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".