The Development of a Simulated Umbilical Line Insertion Model and Curriculum in the Neonatal Intensive Care Unit
Bibliographic record
Abstract
Background Insertion of an umbilical venous catheter (UVC) is a required skill for pediatric residents to learn and perform effectively. However, there is known variability in the ability of residents to perform this essential neonatal skill. Objective The objective of our study was to create a competency-based curriculum for umbilical vein catheter insertion using a human umbilical tissue simulated model, and to assess the feasibility of the curriculum on resident learners during their neonatology rotations. Methods We evaluated the curriculum by assessment of resident learning, reactions, and behaviours. Performance was assessed using the Ottawa Surgical Competency Operating Room Evaluation (O-SCORE). Results A total of 14 residents were included for analysis. The majority were 'senior' residents (postgraduate year (PGY)-3 and PGY-4 n = 10; PGY-1 n =4), and they reported a wide range of previous experience with UVC insertion prior to this curriculum implementation. The residents' reaction to the curriculum was overwhelmingly positive. All residents maintained or improved in their knowledge assessment. O-SCORE results showed improvement in UVC insertion before and after curriculum completion for both junior (2.5 +/- 0.71 to 4.5 +/- 0.41) and senior (3.55 +/- 0.42 to 4.95 +/- 0.15, p < 0.001) residents. The mean improvement in O-SCORE was greater for junior residents than senior residents. Conclusion The results of this study demonstrate the feasibility and emerging impact of a competency-based curriculum using simulation for procedural skills.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".