Advanced closed-loop communication training: the blindfolded resuscitation
Bibliographic record
Abstract
Closed-loop communication (CLC) improves task efficiency and decreases medical errors; however, limited literature on strategies to improve real-time use exist. The primary objective was whether blindfolding a resuscitation leader was effective to improve crisis resource management (CRM) skills, as measured by increased frequency of CLC. Secondary objectives included whether blindfolding affected overall CRM performance or perceived task load. Participants included emergency medicine (EM) or EM/paediatric dual resident physicians. Participants completed presurveys, were block randomised into intervention (blindfolded) or control groups, lead both adult and paediatric resuscitations and completed postsurveys before debriefing. Video recordings of the simulations were reviewed by simulation fellowship-trained EM physicians and rated using the Ottawa CRM Global Rating Scale (GRS). Frequency of CLC was assessed by one rater via video review. Summary statistics were performed. Intraclass correlation coefficient was calculated. Data were analysed using R program for analysis of variance and regression analysis. There were no significant differences between intervention and control groups in any Ottawa CRM GRS category. Postgraduate year (PGY) significantly impacts all Ottawa GRS categories. Frequency of CLC use significantly increased in the blindfolded group (31.7, 95% CI 29.34 to 34.1) vs the non-blindfolded group (24.6, 95% CI 21.5 to 27.7). Participant's self-rated perceived NASA Task Load Index scores demonstrated no difference between intervention and control groups via a Wilcoxon rank sum test. Blindfolding the resuscitation leader significantly increases frequency of CLC. The blindfold code training exercise is an advanced technique that may increase the use of CLC.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".