Abstract 268: Quantitative Performance Assessment of Simulated Pediatric Cardiopulmonary Resuscitation
Bibliographic record
Abstract
Background: Methods to quantitatively measure performance during resuscitative care are lacking in published literature. Members of our group have previously published psychometric analyses of task-based scoring instruments used in educational research in pediatric resuscitation. These published investigations used instruments that were designed for specific cases in pediatric resuscitation, rather than for a more generalizable application. We hypothesize that a novel scoring instrument will reliably assess clinical performance during simulated cardiac arrest. Methods: This study was conducted at 11 pediatric centers in Canada and the US. Teams of pediatric providers performed a simulated cardiac arrest scenario (asystole for 6 minutes, VF for 6 minutes). A task-based scoring instrument was designed by investigator consensus using a 0, 1, or 2 point scoring system to rate performance during cardiac arrest. The items were chosen according to the essential steps in the pulseless arrest algorithm of the AHA Pediatric Advanced Life Support course and include CPR performance parameters (chest compression rate, depth, release, pauses), defibrillation (dose in J/kg, timing), and epinephrine (dose, timing). Multiple raters reviewed and scored a set of simulations. Overall interrater reliability was measured; a fully-crossed generalizability study with team and rater as facets was performed to determine the variance in scores ascribable to each facet; a decision study was done to determine the effect of additional raters and scenarios on the G coefficient. Results: Three raters scored four videos. Overall scores ranged from 53/90 (59%) to 73/90 (81%) possible points. Intraclass correlation coefficient was 0.77 (F 3,8 = 4.46, p = 0.04). Variance components were 21% for rater, 57% for scenario. G coefficient was 0.80; by D study this increased to 0.91 and 0.93 with 8 and 10 raters, respectively. Conclusions: A novel scoring instrument for quantifying performance during pediatric cardiac arrest showed modest reliability and generalizability. Future studies should examine the effect of a larger number of raters and/or scenarios on generalizability, as well as the utility of the instrument in assessing real clinical performance.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".