Performance of the interRAI ED Screener for Risk-Screening in Older Adults Accessing Paramedic Services
Bibliographic record
Abstract
Background Paramedics respond to a significant number of non-emergency calls generated by older adults each year. Paramedics routinely assess and screen older adults to determine risk level and need for additional follow-up. This project implemented the interRAI ED Screener into routine care to determine whether the screener and resulting Assessment Urgency Algorithm (AUA) score is useful in predicting adverse outcomes. Methods We conducted a population-based retrospective study using administrative health data for patients aged 65+ assessed by paramedics from July 2016 to February 2017. Patients were assigned an AUA score and classified into three risk categories. Outcome data including hospitalizations, Emergency Department (ED) visits, home care status, and survival were collected and compared across AUA risk categories using descriptive and analytical statistics. Results Of the 2,801 patients screened, 31.9% were classified as high risk, 23.6% as moderate risk, and 44.6% as low risk. Patients who scored in the highest risk category were found to have longer hospital stays, and were more likely to require home care (p<.0001). The AUA risk category also predicted survival (p<.001). Conclusions The AUA predicted multiple adverse outcomes in this population. Use of the AUA by paramedics may aid in earlier identification of those in need of additional intervention and services.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".