Transcutaneous cardiac pacing competency among junior residents undergoing an ACLS course: impact of a modified high fidelity manikin
Bibliographic record
Abstract
BACKGROUND: Transcutaneous cardiac pacing (TCP) is recommended to treat unstable bradycardia. Simulation might improve familiarity with this low-frequency procedure. Current mannequins fail to reproduce key features of TCP, limiting their usefulness. The objective of this study was to measure the impact of a modified high-fidelity mannequin on the ability of junior residents to achieve six critical tasks for successful TCP. METHODS: First-year residents from various postgraduate programs taking an advanced cardiovascular life support (ACLS) course were enrolled two consecutive years (2015 and 2016). Both cohorts received the same standardized course content. An ALS simulator® mannequin was used to demonstrate and practice TCP during the bradycardia workshop of the first cohort (control cohort, 2015) and a modified high-fidelity mannequin that reproduces key features of TCP was used for the second cohort (intervention cohort, 2016). Participants were tested after training with a simulation scenario requiring TCP. Performances were graded based on six critical tasks. The primary outcome was the successful use of TCP, defined as having completed all tasks. RESULTS: < 0.001). Participants in the intervention cohort were more likely to recognize when pacing was inefficient (86 vs 12%), obtain ventricular capture (48 vs 2%), and check for a pulse rate to confirm capture (48 vs 0%). CONCLUSIONS: TCP is a difficult skill to master for junior residents. Training using a modified high-fidelity mannequin significantly improved their ability to establish TCP during a simulation scenario.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".