Derivation and Validation of the Detection of Indicators and Vulnerabilities for Emergency Room Trips Scale for Classifying the Risk of Emergency Department Use in Frail Community‐Dwelling Older Adults
Bibliographic record
Abstract
OBJECTIVES: To develop and validate a prognostic case finding tool that classifies the risk of emergency department (ED) use in an older home care population. DESIGN: Population-based retrospective cohort study using routinely collected data from home care clinical assessments linked prospectively to ED records. SETTING: Ontario and the Winnipeg Regional Health Authority, Canada. PARTICIPANTS: Older adults living at home and expected to receive in-home services for at least 60 days (N = 361,942). MEASUREMENTS: One or more ED visits within 6 months after an in-home clinical assessment was used as the main dependent measure. Ninety-five person-level risk measures from a clinical assessment instrument were selected as potential independent variables. The Detection of Indicators and Vulnerabilities for Emergency Room Trips (DIVERT) Scale was derived using recursive partitioning analyses informed by a multinational clinical panel. RESULTS: Overall, 41.2% had one or more ED visits within 6 months of their in-home assessment. Previous ED use and cardiorespiratory symptoms, cardiac conditions, and specific geriatric syndromes were predictors within the six-level DIVERT Scale. The scale provided adequate risk differentiation for case finding, with an area under the receiver operating characteristic curve of 0.62 (95% confidence interval = 0.61-0.62) and distinct risk gradients between risk scores. The multilevel validation demonstrated consistent performance across geographic and participant clusters. CONCLUSION: The DIVERT Scale is a valid case-finding tool for ED use in older home care clients. It may be suitable for preemptively and systematically risk-stratifying individuals or groups for additional assessment, case management, and preventative interventions. It may also be suitable for the stratification and adjustment of performance metrics.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".