Emergency Room Evaluation and Recommendations and Risk Screening of Incident Major Neurocognitive Disorders in Older Females: Results of an Observational Population-Based Cohort Study
Bibliographic record
Abstract
Background “Emergency Room Evaluation and Recommendations” (ER2) risk levels (i.e., low, moderate and high) may be used to screen for major neurocognitive disorders (MNCD) in older emergency department users, as a high ER2 risk level is associated with MNCD diagnosis. This study aims to examine the association of ER2 risk levels with incident MNCD in community-dwelling older adults. Methods A total of 709 participants of the EPIDémiologie de l’OStéoporose (EPIDOS) study—an observational population-based cohort study—were recruited in Toulouse (France). ER2 low, moderate and high risk levels were determined at baseline. Incident MNCD and their type (i.e., Alzheimer’s disease (AD) vs. non-AD) were diagnosed after a 7-year follow-up period. Results The overall incidence of MNCD was 29.1%. A low ER2 risk level was associated with low incidence of MNCD [Hazard ratio (HR) = 0.71 with P = 0.018] and AD (HR = 0.56 with P = 0.003), whereas a high risk level, both individually and when combined with a moderate risk level, was associated with high incidence of MNCD (HR ≥ 1.40 with P ≤0.018) and AD (HR ≥ 1.80 with P ≤ 0.003). No association was found with incident non-AD. Conclusion ER2 risk levels were positively associated with incident MNCD in EPIDOS participants, suggesting that ER2 may be used for risk screening of MNCD in the older population.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".