Validation of the Lusaka Formula: A Novel Formula for Weight Estimation in Children Presenting for Surgery in Zambia
Bibliographic record
Abstract
BACKGROUND: In children, the use of actual weight or predicted weight from various estimation methods is essential to reduce harm associated with dosing errors. This study aimed to validate the new locally derived Lusaka formula on an independent cohort of children undergoing surgery at the University Teaching Hospital in Lusaka, Zambia, to compare the Lusaka formula's performance to commonly used weight prediction tools and to assess the nutritional status of this population. METHODS: The Lusaka formula (weight = [age in months/2] + 3.5 if under 1 year; weight = 2×[age in years] + 7 if older than 1 year) was derived from a previously published data set. We aimed to validate this formula in a new data set. Weights, heights, and ages of 330 children up to 14 years were measured before surgery. Accuracy was examined by comparing the (1) mean percentage error and (2) the percentage of actual weights that fell between 10% and 20% of the estimated weight for the Lusaka formula, and for other existing tools. World Health Organization (WHO) growth charts, mid upper arm circumference (MUAC), and body mass index (BMI) were used to assess nutritional status. RESULTS: The Lusaka formula had similar precision to the Broselow tape: 160 (48.5%) vs 158 (51.6%) children were within 10% of the estimated weight, 241 (73.0%) vs 245 (79.5%) children were within 20% of the estimated weight. The Lusaka formula slightly underestimated weight (mean bias, -0.5 kg) in contrast to all other predictive tools, which overestimated on average. Twenty-two percent of children had moderate or severe chronic malnutrition (stunting) and 4.7% of children had moderate or severe acute malnutrition (wasting). CONCLUSIONS: The Lusaka formula is comparable to, or better than, other age-based weight prediction tools in children presenting for surgery at the University Teaching Hospital in Lusaka, Zambia, and has the advantage that it covers a wider age range than tools with comparable accuracy. In this population, commonly used aged-based prediction tools significantly overestimate weights.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".