P017: Impact of the use of a checklist for transcutaneous cardiac pacing on competency of junior residents undergoing an advanced cardiac life support course
Bibliographic record
Abstract
Introduction: Transcutaneous cardiac pacing (TCP) is recommended for the treatment of symptomatic bradycardia, a life-threatening condition. Although TCP is taught in ACLS (advanced cardiac life support) courses, it is a difficult skill to master for junior residents. The main objective of this study is to measure the impact of having access to a checklist on successful TCP implementation. Our hypothesis was that the availability of a CL would improve performance of junior residents in the management of symptomatic bradycardia by facilitating TCP. Methods: We conducted a prospective, randomized, single-site study. First-year residents entering postgraduate programs and taking a mandatory ACLS course were enrolled. Students had didactic sessions on the management of symptomatic bradycardia followed by hands-on teaching on a low-fidelity manikin (ALS® simulator, Laerdal) using a CL conceived for this project as a teaching tool. Study participants were then assessed with a simulation scenario requiring TCP. Participants were randomly assigned to groups with and without CL accessibility. Performances were graded on six critical tasks. The primary outcome was the successful use of TCP, defined as having completed all tasks. Participants then completed a post-test questionnaire. Sample size estimation was based on a previous project (Ranger et al., 2018). Accepting an alpha error of 0.05 and a power of 80%, 45 participants in each group would permit the detection of 26.5% in performance gain. Results: Of 250 residents completing the ACLS course in 2017, 85 voluntary participants were randomized to a control group (no CL available during testing, n = 42) or an experimental group (CL available during testing, n = 43). Six participants in the experimental group adequately used TCP compared to five participants in the control group (p = 0.81, chi-squared test). Out of the 43 participants who had access to the CL, only 2 (5%) used it. Reasons why the CL was infrequently used were stated as the following: 24 participants (56%) mentioned not realizing it was available, 8 (19%) considered it was of little to no utility and 5 (19%) forgot a CL existed. Conclusion: Availability of a checklist previously used during simulation teaching did not increase junior residents’ capacity to correctly apply TCP. Non-recognition of CL availability and decreased perceived need for it were the main reasons for marginal use. Our results suggest that there are many limiting factors to CL effectiveness.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".