Ambulance crew‐initiated non‐conveyance in the Helsinki EMS system—A retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Ambulance patients are usually transported to the hospital in the emergency medical service (EMS) system. The aim of this study was to describe the non-conveyance practice in the Helsinki EMS system and to report mortality following non-conveyance decisions. METHODS: All prehospital patients ≥16 years attended by the EMS but not transported to a hospital during 2013-2017 were included in the study. EMS mission- and patient-related factors were collected and examined in relation to patient death within 30 days of the EMS non-conveyance decision. RESULTS: The EMS performed 324,207 missions with a patient during the study period. The patient was not transported in 95,909 (29.6%) missions; 72,233 missions met the study criteria. The patient mean age (standard deviation) was 59.5 (22.5) years; 55.5% of patients were female. The most common dispatch codes were malaise (15.0%), suspected decline in vital signs (14.0%), and falling over (12.9%). A total of 960 (1.3%) patients died within 30 days after the non-conveyance decision. Multivariate logistic regression analysis revealed that mortality was associated with the patient's inability to walk (odds ratio 3.19, 95% confidence interval 2.67-3.80), ambulance dispatch due to shortness of breath (2.73, 2.27-3.27), decreased level of consciousness (2.72, 1.75-4.10), decreased blood oxygen saturation (2.64, 2.27-3.06), and abnormal systolic blood pressure (2.48, 1.79-3.37). CONCLUSION: One-third of EMS missions did not result in patient transport to the hospital. Thirty-day mortality was 1.3%. Abnormalities in multiple respiratory-related vital signs were associated with an increased likelihood of death within 30 days.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".