Anthropometric measures are simple and accurate paediatric weight-prediction proxies in resource-poor settings with a high HIV prevalence
Bibliographic record
Abstract
Rationale Accurate weight measurements are essential for both growth monitoring and drug dose calculations in children. Weight can be accurately measured using calibrated scales in resource-rich settings; however, reliable scales are often not available in resource-poor regions or emergency situations. Current age and/or length/height-based weight-prediction equations tend to overestimate weight because they were developed from Western children's measures. Objective To determine the accuracy of several proxy measures for children's weight among a predominately HIV-positive group of children aged 18 months to 12 years in Botswana. Design Weight, length/height, ulna and tibia lengths, mid-upper arm circumference (MUAC) and triceps skinfold were measured on 775 children recruited from Gaborone, Botswana, between 6 July and 24 August 2011. Results Mean (95% CI) age and weight were 7.8 years (7.5 to 8.4) and 21.7 kg (21.2 to 22.2), respectively. The majority of children were HIV-positive (n=625, 81%) and on antiretroviral treatment (n=594, 95%). The sample was randomly divided; a general linear model was used to develop weight-prediction equations for one half of the sample (n=387), which were then used to predict the weight of the other half (n=388). MUAC and length/height, MUAC and tibia length and MUAC and ulna length most accurately predicted weight, with an adjusted R2of 0.96, 0.95 and 0.93, respectively. Using MUAC and length/height, MUAC and tibia length and MUAC and ulna length equations, ≥92% of predicted weight fell within 15% of actual weight, compared with <55% using current equations. Conclusion The development of nomograms using these equations is warranted to allow for rapid and accurate weight prediction from these simple anthropometric measures in HIV-endemic, resource-constrained settings.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".