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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.037 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".