Comparison of the Medical Priority Dispatch System to an Out‐of‐hospital Patient Acuity Score
Bibliographic record
Abstract
BACKGROUND: Although the Medical Priority Dispatch System (MPDS) is widely used by emergency medical services (EMS) dispatchers to determine dispatch priority, there is little evidence that it reflects patient acuity. The Canadian Triage and Acuity Scale (CTAS) is a standard patient acuity scale widely used by Canadian emergency departments and EMS systems to prioritize patient care requirements. OBJECTIVES: To determine the relationship between MPDS dispatch priority and out-of-hospital CTAS. METHODS: All emergency calls on a large urban EMS communications database for a one-year period were obtained. Duplicate calls, nonemergency transfers, and canceled calls were excluded. Sensitivity and specificity to detect high-acuity illness, as well as positive predictive value (PPV) and negative predictive value (NPV), were calculated for all protocols. RESULTS: Of 197,882 calls, 102,582 met inclusion criteria. The overall sensitivity of MPDS was 68.2% (95% confidence interval [CI] = 67.8% to 68.5%), with a specificity of 66.2% (95% CI = 65.7% to 66.7%). The most sensitive protocol for detecting high acuity of illness was the breathing-problem protocol, with a sensitivity of 100.0% (95% CI = 99.9% to 100.0%), whereas the most specific protocol was the one for psychiatric problems, with a specificity of 98.1% (95% CI = 97.5% to 98.7%). The cardiac-arrest protocol had the highest PPV (92.6%, 95% CI = 90.3% to 94.3%), whereas the convulsions protocol had the highest NPV (85.9%, 95% CI = 84.5% to 87.2%). The best-performing protocol overall was the cardiac-arrest protocol, and the protocol with the overall poorest performance was the one for unknown problems. Sixteen of the 32 protocols performed no better than chance alone at identifying high-acuity patients. CONCLUSIONS: The Medical Priority Dispatch System exhibits at least moderate sensitivity and specificity for detecting high acuity of illness or injury. This performance analysis may be used to identify target protocols for future improvements.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".