Validity and risk factor analysis for helicopter emergency medical services in Japan: a pilot study
Bibliographic record
Abstract
BACKGROUND: Some emergency departments use triage scales, such as the Canadian Triage and Acuity Scale and Japan Urgent Stroke Triage Score, to detect life-threatening situations. However, these protocols have not been used for aeromedical services. Therefore, we investigated the factors predicting these life-threatening situations in aeromedical services as a pilot study for establishing the protocol. METHOD: We retrospectively evaluated helicopter emergency medical service cases from 1 April 2015 to 31 March 2020 at Gifu University Hospital using the mission records. We only evaluated cases dealing with suggested internal medicine issues. We excluded cases influenced by external factors such as trauma or cases that included hospital-to-hospital transportation, focusing only on prehospital care. We evaluated the validity of the medical emergencies based on the needs for emergency interventions and hospital admission and of the suggested diagnoses and associated risk factors. RESULT: A total of 451 cases were suitable for inclusion in the study. In the analysis for all emergency calls, 235 (52.11%) cases needed emergency intervention and 300 (64.4%) required hospital admission. The suggested diagnosis was valid for 261 (57.87%) cases. After the first assessment by emergency medical technicians, 75 cases were removed. Analysis after this first assessment found that 52.31% cases required emergency intervention, 70.26% needed admission, and the suggested diagnosis was valid for 69.41% of cases. In the analysis of emergency calls, the multivariate analysis of some key variables identified age, playing sports, and gasping as risk factors for emergency intervention. Hospital admission risk factors included being age only. The suggested diagnosis was valid only for sports situations. In the analysis after the first assessment by an emergency medical technician, risk factors for emergency intervention included being age being male, playing sports, and gasping, and those for hospital admission was being age, being male, and experiencing stroke symptoms and/or disturbance of consciousness. The suggested diagnosis was valid only for sports situations. CONCLUSION: Some 'second' keywords/phrases predict medical emergencies. Therefore, the dispatch commander should gather these keyword/phrases to assess.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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".