Optimizing prehospital triage: Web‐based tools for severity classification in prehospital care, prospective study in Northern Thailand
Bibliographic record
Abstract
Abstract Background Emergency medical services act as the frontline in patient care. They triage patients' severity and initial treatment and refer them to the proper hospital. Incorrect triage can lead to adverse outcomes. Objective The study's main objective is to examine the agreement between the prehospital triage level and the emergency department (ED) triage level in the same patients. The secondary outcome is to predict admission. Methods A prospective study was performed. The severity of triage levels at the scene was determined by the Advanced Life Support (ALS) team using the Chiang Mai University (CMU) prehospital triage webpage. Then they were compared with the initial Canadian Triage and Acuity Scale (CTAS) level at the ED using kappa correlation. This study was performed from April 1 to August 31, 2023. Admissions were predicted utilizing the CMU prehospital triage webpage. The data were analyzed for sensitivity and specificity. Results The analysis included 176 patients who were triaged by the ALS team. There was a fair agreement between the scene triage by CMU prehospital triage webpage and ED triage by CTAS (kappa = 0.391, 95% CI = 0.309–0.479, p ‐value < 0.005). The CMU prehospital triage webpage predicted 73.79% of admissions (95% CI = 64.20–81.96) and 71.23% specificity (95% CI = 59.45–81.23). Conclusion There was a fair agreement between the scene triage by CMU prehospital triage webpage and ED triage by CTAS. However, the CMU prehospital triage webpage has good sensitivity and specificity for predicting admission.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".