Randomised simulation trial found an association between rescuers' height and weight and chest compression quality during paediatric resuscitation
Bibliographic record
Abstract
AIM: Our aim was to examine the relationship between rescuers' anthropometric data and chest compression quality during paediatric resuscitation training. METHODS: This study focused on 224 medical students (53% women) who performed 2 minutes of paediatric resuscitation at the Medical University of Vienna, Austria: 116 on a baby manikin and 108 on an adolescent manikin. Skill Reporter software measured chest compression quality by recording compression depth, frequency, hand position and complete recoil. The participants' height, weight and body mass index (BMI) were recorded. RESULTS: Participants with a lower BMI achieved higher total chest compression scores on both the baby and adolescent manikins than participants with a higher BMI. The latter were more likely to exceed the correct compression depth and not achieve complete chest recoil in the adolescent manikin. When it came to the baby manikin, the female participants achieved better chest recoil and the males achieved a higher number of compressions at the correct rate. Males also achieved better chest recoil with the adolescent manikins. Being tall only correlated with incomplete recoil in the adolescent manikin. CONCLUSION: The results indicate that anthropometric variables were associated with chest compression quality in paediatric patients and should be considered by future education programmes.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".