Adjustments for weighing clothed babies at high altitude or in cold climates
Bibliographic record
Abstract
Public health nutritionists rely on anthropometry for nutritional assessment, program planning, and evaluation. Children are usually heavily clothed at high altitudes and in cold climates. Failing to adjust for clothing weight could underestimate malnutrition prevalence. The objective of this paper is to validate an adjustment process for estimating clothing weight and quantify potential misclassification error. In March and September 2009, 293 and 272 children under 2 years of age, respectively, were measured for weight and length in 14 highlands communities in Ecuador. Weight-for-age z-scores (WAZ) and weight-for-height z-scores (WHZ) were compared using clothing-unadjusted weights and two types of clothing-adjusted weights: individual clothing-weights and population-mean clothing-weights. Modelling showed up to 24% of children's nutritional status and degree of malnutrition were misclassified for WAZ, and 13% for WHZ, when clothing was not taken into account in this cold climate. Compared with the more time-intensive individual clothing-weight adjustment, the population-mean clothing-weight adjustments had high specificity and sensitivity for WAZ. In cold climates, adjusting for population mean clothing weight provides a better estimate of the prevalence of malnutrition to inform appropriate program decisions for addressing underweight. An individual clothing weight adjustment may also be essential to classify a specific child's nutritional status when acute malnutrition is a concern.
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 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) | 0.000 | 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) | 0.000 | 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".