A Novel Code Team Leader Card to Improve Leader Identification
Bibliographic record
Abstract
Prompt and clear code team leader identification is vital in effective cardiopulmonary resuscitation (CPR), and pediatric trainees often have limited experience in these scenarios. This project sought to develop a tangible object that provided clear leader identification and assisted in code team management and simulated team training. A Code Team Leader Card (CTLC) was designed to provide clear leader identification while simultaneously providing a cognitive aid via integration of pediatric advanced life support (PALS) algorithms. Additionally, CTLC served to occupy the leader's hands to limit their ability to intervene on procedural tasks. The CTLC was incorporated into pediatric resident simulation training, and pre- and postintervention survey data were analyzed. Analysis particularly focused on whether "a leader was clearly identified by all team members." The relationship between CTLC implementation and consistent leader recognition was evaluated using chi-squared test, and secondary qualitative data were obtained via debriefing sessions. Pediatric residents completed 131 surveys prior to CTLC implementation and 41 surveys after implementation. Consistent code team leader recognition increased significantly from 61.8% (81 of 131) pre-CTLC to 80.5% (33 of 41) after introduction of CTLC (P=0.027). Participants commented on the benefits of CTLC during debriefing sessions. Use of a CTLC significantly improved leader recognition during simulated CPR. Inclusion of PALS algorithms led to normalization and increased utilization of these adjunct materials. The CTLC provided a secondary benefit of occupying the leader's hands, thereby allowing that person to focus on overseeing the team rather than assisting with procedural tasks.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.007 | 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".