Evaluation of a Pediatric Mock Code Educational Training Program at a Large, Tertiary Care Pediatric Hospital
Bibliographic record
Abstract
Background: Management of the acutely ill children represents one of the more complex clinical skills required of pediatric physicians. Our goal was to develop and evaluate a multidisciplinary pediatric mock code training program for the pediatric residents in our institution. Methods: We performed a before and after evaluation of pediatric residents. The residents were educated by attending five mock code scenarios, followed by debriefing. Before and after the five sessions, the residents completed a self-assessment questionnaire. Results: Residents reported a significant improvement in their comfort in all aspects of managing pediatric resuscitations, with notable improvement seen in running a resuscitation requiring airway management, managing fluid resuscitation and performing endotracheal intubation. The most prominent change was demonstrated in the comfort level of the overall management of a pediatric resuscitation. Conclusion: The pediatric mock code educational training program improved residents’ self-reported knowledge and comfort level in managing pediatric emergency situations.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".