Assessing Severity of Illness in Patients Transported to Hospital by Paramedics: External Validation of 3 Prognostic Scores
Bibliographic record
Abstract
Introduction: Emergency Medical Services (EMS) are the first healthcare contact for the majority of severely ill patients. Physiologic measures collected by EMS, when incorporated into a prognostic score, may provide important information on patient illness severity. This study compares the predictive ability of 3 common prognostic scores for predicting clinical outcomes in EMS patients. Methods: Discrimination and calibration for predicting the primary outcome of hospital mortality, and secondary outcomes of 2-day mortality and ED disposition, were assessed for each of the scores using a one-year cohort of patients transported to hospital by EMS in Alberta, Canada. For each score, binary logistic regression was used to predict hospital mortality and 2-day mortality and ordinal logistic regression was used to predict ED disposition. Discrimination for each outcome was assessed using C-statistics, and calibration was assessed using calibration curves comparing predicted versus observed outcomes. Results: The Critical Illness Prediction [CIP], Modified Early Warning Score [MEWS], and National Early Warning Score [NEWS] were compared using 121,837 adult patients who were transported by paramedics. All scores had good discrimination for hospital mortality (C-statistic CIP: 0.79, MEWS: 0.71, NEWS: 0.78) and 2-day mortality (CIP:0.85, MEWS: 0.80, NEWS:0.85) but only moderate discrimination for ED disposition (CIP: 0.68, MEWS: 0.61, NEWS: 0.66). Calibration was reliable for hospital mortality in all scores but over-predicted risk for 2-day mortality at higher scores. Overall, the CIP score had the best discrimination, good calibration, and the greatest range of predicted probabilities (0.01 at a CIP score of 0 to 0.92 at a CIP score of 8) for hospital mortality. Conclusions: Prognostic scores using physiologic measures assessed by paramedics have good predictive ability for hospital mortality. These scores, particularly the CIP score, may be considered as a tool for mortality risk stratification or as a general measure of illness severity for patients included in EMS studies.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".