Impact of Operator Medical Specialty on Endotracheal Intubation Rates in Prehospital Emergency Medicine—A Retrospective Cohort Study
Bibliographic record
Abstract
Prehospital endotracheal intubation (ETI) can be challenging, and the risk of complications is higher than in the operating room. The goal of this study was to compare prehospital ETI rates between anaesthesiologists and non-anaesthesiologists. This retrospective cohort study compared prehospital interventions performed by either physicians from the anaesthesiology department (ADP) or physicians from another department (NADP, for non-anaesthesiology department physicians). The primary outcome was the prehospital ETI rate. Overall, 42,190 interventions were included in the analysis, of whom 68.5% were performed by NADP. Intubation was attempted on 2797 (6.6%) patients, without any difference between NADPs and ADPs (6.5 versus 6.7%, p = 0.555). However, ADPs were more likely to proceed to an intubation when patients were not in cardiac arrest (3.4 versus 3.0%, p = 0.026), whereas no difference was found regarding cardiac arrest patients (65.2 versus 67.7%, p = 0.243) (p for homogeneity = 0.005). In a prehospital physician-staffed emergency medical service, overall ETI rates did not depend on the frontline operator’s medical specialty background. ADPs were, however, more likely to proceed with ETI than NADPs when patients were not in cardiac arrest. Further studies should help to understand the reasons for this difference.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".