Caregiver Characteristics Associated With Quality of Cardiac Compressions on an Adult Mannequin With Real-Time Visual Feedback
Bibliographic record
Abstract
INTRODUCTION: Chest compression (CC) quality directly impacts cardiac arrest outcomes. Provider body type can influence the quality of cardiopulmonary resuscitation (CPR); however, the magnitude of this impact while using visual feedback is not well described. The aim of the study was to determine the association between provider anthropometric variables on fatigue and CC adherence to 2015 American Heart Association CPR while receiving visual feedback. METHODS: This was a planned secondary analysis of healthcare professionals from multiple hospitals performing continuous CC for 2 minutes on an adult CPR mannequin with dynamic visual feedback. Main outcome measures include compression data (depth, rate, and lean) evaluated in 30-second epochs to explore performance fatigue. Multivariable models examined the relationship of provider anthropometrics to CC quality. Binomial mixed effects models were used to characterize fatigue by examining performance for 4 epochs. RESULTS: Three hundred seventy-seven 2-minute CC episodes were analyzed. Extreme (low and high) BMI and weight are associated with poorer CC. Larger size (height, weight, and BMI) is associated with better depth but worse lean compliance. Performance fatigued for all providers for 2 minutes, but shorter, lighter weight, female participants had the greatest decline. On multivariable analysis, rate compliance did not deteriorate regardless of provider anthropometrics. CONCLUSIONS: Anthropometrics impact provider CC quality. Despite visual feedback, variable effects are seen on compression depth, rate, recoil, and fatigue depending on the provider sex, weight, and BMI. The 2-minute interval for changing chest compressors should be reconsidered based on individual provider characteristics and risk of fatigue's impact on high-quality CPR.
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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 | 0.001 | 0.009 |
| 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) | 0.004 | 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".