[Simulation Training for Critical Obstetric Hemorrhage in Cesarean Section for Medical Students with Human Patient Simulator (HPS®), Comparison between Role-play and Impromptu Simulation[.
Bibliographic record
Abstract
BACKGROUND: The simulation training for critical obstetric hemorrhage for medical students, lacks a gold standard, but should be effectively performed. To opti- mize the simulation for critical obstetric hemorrhage with human patient simulator (HPS® Human Patient Simulator Muse2.1, CAE Healthcare, Quebec, Canada), we assessed the effectiveness of impromptu simulation and role-play simulation among fifth-year medical stu- dents. METHODS: The role-play simulation among 49 medi- cal students, of obstetric critical hemorrhage in Cesar- ean section was compared with the learning effect of the unprepared impromptu simulation among other 49 medical students. (observational cohort study). The effects of simulation training was assessed with pre and post numbers of answers for the question "the management items for critical obstetric hemorrhage in cesarean section". RESULTS: The items of answer pre- and post-simula- tion were, oxygen administration, uterotonic drugs, infusion, preparation of the blood products, blood sam- ple examination, cross-matching test ensuring of the manpower, plasma substitute administration, vasopres- sors, blood transfusion, intraoperative blood salvage, interventional radiology, arterial line, central venous catheter, airway management general anesthesia and total hysterectomy. Simulation provided a learning effect for these items. In impromptu simulation, the numbers of answers per one medical student were increased from 2.3 ±1.4 to 7.0±3.0 items (P<0.0001). In another role-play simulation, those were increased from 2.1±1.8 to 5.6±2.3 items (P<0.0001). CONCLUSIONS: The impromptu simulation was con- sidered to have a superior learning effect than role-play simulation.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".