Best Guess method: A further external validation study and comparison with other methods
Bibliographic record
Abstract
OBJECTIVE: Validation of the 'Best Guess' weight estimation method on a geographically divergent external sample of children, plus comparison with APLS and Broselow weight estimation methods. METHODS: Prospective cross-sectional analytical study at Princess Margaret Hospital Emergency Department. A convenience sample of children aged 0-14 years recruited from May to June 2008. Age, sex, ethnicity, height and actual weight obtained. Agreement between the methods is reported as a comparative mean and distribution of the percentage error, plus the proportion of instances where the error exceeded 20% of the measured weight. RESULTS: A total of 1235 children were included. The 'Best Guess' method was the most accurate, particularly in children aged 1-4 years (mean percentage error +1.69%). In other age groups it overestimated weight, with mean percentage errors ranging from 3.41% to 6.25%. Across all age groups the Broselow method was most precise, with tendency to underestimate weight across age groups with mean percentage errors ranging from -5.28% to -7.24%. The APLS method was least accurate and precise, with mean percentage errors ranging from -12.61% to -17.36%. Net weight underestimation errors exceeding 20% were associated with increased mean body mass index. CONCLUSION: The Best Guess weight estimation method is accurate, especially in children aged 1-4 years. It moderately overestimates weight in other ages. The Broselow method was more precise, whereas the APLS method was the least accurate and precise of all. The ease of use of the Broselow method argues for greater use in the ED and prehospital setting.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.008 | 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 teacher head, 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".