Validation of the Triage Risk Stratification Tool to Identify Older Persons at Risk for Hospital Admission and Returning to the Emergency Department
Bibliographic record
Abstract
OBJECTIVES: To assess the predictive validity of the Triage Risk Stratification Tool (TRST) to identify return to the emergency department (ED) or hospitalization in a multicenter patient sample. DESIGN: Prospective, observational study with 1-year follow-up. SETTING: EDs of three hospitals in Toronto, Canada. PARTICIPANTS: Seven hundred eighty-eight subjects aged 65 to 101 (mean age 76.6, 58.5% female) who presented to the ED and were discharged home from the ED. MEASUREMENTS: Trained clinical assessors completed the TRST on patients aged 65 and older during a 4-week study period. Patients who subsequently returned to the ED or were admitted to the hospital were identified using hospital information systems and classified as experiencing the composite endpoint at 30, 120, and 365 days. RESULTS: The mean TRST score was 1.55 (range 0-5), and 147 (18.7%) patients experienced the composite endpoint of return to the ED or hospital admission by 30 days. The sensitivity of a TRST score of 2 or greater was 62%, (95% confidence interval (CI)=54-70%), specificity was 57% (95% CI=53-61%), and likelihood ratio was 1.44 (95% CI=1.23-1.66). The area under the curve was 0.61 using a cutoff score of 2. CONCLUSION: The TRST demonstrated only moderate predictive ability, and ideally, a better prediction rule should be sought. Future studies to develop better prediction rules should compare their performance with that of existing prediction rules, including the TRST and Identifying Seniors at Risk tool, and assess the effect of any new prediction rule on patient outcomes.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".