Improving internal medicine residents' performance, knowledge, and confidence in central venous catheterization using simulators
Bibliographic record
Abstract
BACKGROUND: Efficacy of simulators in teaching central venous catheterization (CVC) in an internal medicine residency program is unknown. OBJECTIVE: To determine whether or not learning CVC on simulators is associated with improvement in performance of CVC, knowledge about the procedure, and self-reported confidence. METHODS: All consenting first-year internal medicine residents who completed training in CVC on simulators were included. Participants were evaluated pre- and post-training by video-recorded CVC insertion and multiple-choice knowledge assessments. Procedural technique was rated in a blinded fashion by two independent adjudicators. Knowledge retention and self-reported confidence were reassessed at 18 months. MEASUREMENTS: Primary outcome of CVC performance was assessed based on global rating score (minimum 1, maximum 5). Secondary measures include checklist score (out of ten), knowledge score and self-reported confidence (6-point Likert scale ranging from "none" to "complete"). RESULTS: Median global rating scores in 30 participants increased from 3.5 (IQR = 3-4) to 4.5 (IQR = 4-4.5) (P < 0.001). Checklist score increased from 9 (IQR = 6-9.5) to 9.5 (IQR = 9-9.5) (P < 0.001). Knowledge score increased from 65.7 +/- 11.9% to 81.2 +/- 10.7% (P < 0.001). Confidence increased from 3 ("moderate", IQR = 2-3) to 4 ("good", IQR=3-4) (P < 0.001). Sixteen participants completed the retention tests. Improvement in knowledge score and confidence at 18 months was retained compared with baseline (P = 0.002 and P < 0.0001 respectively). CONCLUSIONS: Use of simulators in teaching CVC in an internal medicine residency program results in improved procedural performance, knowledge, and self-reported confidence. Improvement in knowledge and confidence was retained at 18 months.
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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 | 0.011 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".