Incorporating stressors during simulated neonatal endotracheal intubation creates a stress response but does not affect performance: a randomised pilot study
Bibliographic record
Abstract
OBJECTIVE: Investigate the impact of high stress (HS) using four stressors (physiological, psychological/social, contextual and situational) versus low stress (LS) on stress response and performance during simulated neonatal endotracheal intubation (ETI). DESIGN: Non-blinded crossover simulated randomised controlled trial. Subjects included paediatric and neonatology residents. Participants were exposed to HS and LS neonatal ETI scenarios. Primary outcomes were stress response measures: (1) physiologic: heart rate (HR) and HR variability (HRV), (2) psychologic: State-Trait Anxiety Questionnaire (STAI) responses and (3) endocrine: salivary cortisol. These were measured at baseline and pre/during/post each scenario. Secondary outcomes were intubation success rate, duration, and performance on a neonatal intubation checklist. RESULTS: 48 participants completed two scenarios. The HS scenario had a higher HR during (104±15 vs 100±15, mean difference 5 (1-9), p=0.03) and post (97±18 vs 93±15, mean difference 4 (0-9), p=0.04) compared with LS scenario. HRV was not different between groups. STAI trait scores did not differ, but STAI state scores were higher in the HS-post state compared with the LS-post state (38±8 vs 34±7, mean difference 4 (2-6), p=0.001). There was no significant difference in salivary cortisol between scenarios. Success rate, duration and checklist scores did not differ between scenarios. CONCLUSIONS: It is possible to generate a modest physiologic and psychologic stress response in simulated neonatal ETI using a combination of stressors, although without raising salivary cortisol or affecting performance.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".