ABSTRACT 918
Bibliographic record
Abstract
Background and aims: Massive hemorrhage (MH) is a clinical situation that necessitates the ability to work in interdisciplinary team. Aims: To evaluate the application of a MH protocol and the ability to work in interdisciplinary teams using simulation and targeted training. Methods: This prospective observational study was held at the simulation lab of a tertiary health care facility. Participants were nurses, respiratory therapists, orderlies, anesthetists, obstetricians, pediatric emergency physicians, intensivists and hematologists. Teams were submitted to high fidelity simulated MH scenarios. All sessions were videotaped. Targeted training consisted of a debriefing session and a presentation reviewing the protocol as well as teamwork skills. Documents were given during the first session to prepare for the post session two weeks later. Confidence questionnaires were filled out during both sessions. Four blinded independent trained raters reviewed the videos and assessment of performance was done using two checklists. Performances scores were compared using an ANOVA test; confidence questionnaires were compared using a Mann Whitney test. IRB approval was obtained for this study. Results: Sixty-two healthcare professionals involved in 8 interdisciplinary teams participated in the study. Following training, scores for the application of the MH protocol improved by 24% (10-39), p=.002. Scores for the ability to work in teams improved by 17% (6-28), p=0.006. Confidence levels in the ability to apply the MH protocol and work in teams improved by 13% (11-16), p<0.0001. Conclusions: Targeted training using simulation improved participant ability to apply the MH protocol, improved interdisciplinary teamwork skills and confidence levels among participants from all disciplines.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.795 | 0.667 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".