Estimating the Weight of Children During Simulated Emergency Situations Using the Broselow Tape
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to evaluate whether residents can accurately estimate children's weight using the Broselow tape. METHOD: We conducted a preplanned secondary analysis from an experimental trial. Participants were residents in pediatrics, family medicine, and emergency medicine rotating in the ED. Residents were randomly assigned to 2 sets of paired scenarios during 2 sessions. They were asked to estimate the weight of a manikin using the Broselow tape at the beginning of each scenario. The first scenario from the initial session and the last scenario from the second session were used for the current study. The primary analysis was the proportion of participants who accurately estimated manikin weight within a 10% margin of error. RESULTS: Forty residents were recruited. Thirty-two (80%) reported knowledge of the Broselow tape and 13 (32.5%) reported previous use. Weight estimation was accurate in 60% (24/40; 95% confidence interval [CI], 45%-74%) during the first scenarios. Error in weight estimation differed by greater than 25% in 28% (11/40). Error in estimation was not associated with previous knowledge (odds ratio, 6.2; 95% CI, 0.68-56) or previous use (odds ratio, 0.9; 95% CI, 0.23-3.5) of the Broselow tape. In the last scenario, 88% accurately estimated manikin weight (35/40; 95% CI, 73%-95%). CONCLUSIONS: Although most residents reported knowledge of the Broselow tape, 40% made erroneous weight estimations by at least 10% with the first use in this simulation study. With repeated use, they improved significantly over time. Teaching appropriate use of the Broselow tape should be part of residency-training curricula and pediatric advanced life support course.
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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 | 0.007 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".