CIRCumcision learning experience using simulation: A pilot learning platform for safe neonatal circumcision training offered either virtually or in person
Bibliographic record
Abstract
Background: To our knowledge, no formal training combining didactic learning, simulation, and hands-on performance is available for practitioners performing neonatal circumcision. The absence of structured training may result in avoidable complications such as bleeding and penile injury. Herein, we present the results of a pilot neonatal circumcision training platform, offered either virtually or in person. Material and methods: CIRCLES (CIRCumcision Learning Experience using Simulation) consist of 1. online didactic learning; 2. live simulation practice (in person or virtual coaching), and 3. clinical performance. Outcome measures included pre- and post-knowledge scores, self-efficacy questionnaires, and skill assessments of simulation and clinical performance (Likert rating). Face validity for training success was determined by an 80% passing score on the knowledge test and > 75% (mostly independent) performance. Results: For this pilot, we restricted enrolment to seven pediatric residents and one nurse practitioner. Wilcoxon Sum Rank test for non-parametric paired samples for pre-and post-knowledge tests showed a median increase of 20 points in post-knowledge tests (p=0.011). Upon completion of the simulation training, all participants (8/8) have chosen to perform circumcision with the GOMCO clamp. Both in-person (4/4) and virtual participants (4/4) performed >75% of simulation and clinical circumcision independently. Post-training self-efficacy Z scores were higher than pre-training scores, except for the management of bleeding. Conclusion: The pilot CIRCLES learning shows face validity for both in-person and virtual training for neonatal circumcision. We plan to extend this platform to include more trainees and to offer them to established practitioners. The availability of formal training may ultimately reduce adverse outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".