Risk orientation predicts hypoxic time during difficult airway simulation: a mixed-methods pilot study
Bibliographic record
Abstract
Personality factors may explain some of the practice variation observed in medicine. In this pilot study, we used simulation to investigate the relationship between risk orientation and airway management. We hypothesised that higher risk tolerance would predict earlier intervention. Ten emergency medicine residents from the University of Alberta participated in a standardised difficult airway simulation. There was a constant rate of oxygen desaturation necessitating eventual airway intervention. A debriefing interview and a risk orientation questionnaire followed. Time of hypoxia prior to intervention was the outcome measure. Audio interview transcripts underwent thematic analysis. Nine participants were included; one did not complete the simulation as instructed. Higher risk tolerance predicted longer hypoxic time prior to intubation (r=0.72, p=0.03). Theme analysis revealed consistent fears regarding patient instability and chances of a failed airway intervention. Patient instability was emphasised more so by those who intervened earlier. We show that personality characteristics influence resuscitation decision-making at an early stage of training. Trainees may therefore be susceptible to certain types of medical error based on their risk aversion. Implications for resident training, care quality and patient safety are discussed.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".