Reliability of anthropometric measures in a longitudinal cohort of patients initiating ART in West Africa
Bibliographic record
Abstract
BACKGROUND: Anthropometric measurements are a non invasive, inexpensive, and suitable method for evaluating the nutritional status in population studies with relatively large sample sizes. However, anthropometric techniques are prone to errors that could arise, for example, from the inadequate training of personnel. Despite these concerns, anthropometrical measurement error is seldom assessed in cohort studies. We describe the reliability and challenges associated with measurement of longitudinal anthropometric data in a cohort of West African HIV+ adults . METHODS: In a cohort of patients initiating antiretroviral treatment in Mali, we evaluated nutritional status using anthropometric measurements(weight, height, mid-upper arm circumference, waist circumference and triceps skinfold). Observers with no prior experience in the field of anthropometry were trained to perform anthropometrical measurements. To assess the intra- and inter-observer variability of the measurements taken in the course of the study, two sub-studies were carried out: one at the beginning and one at the end of the prospective study. Twelve patients were measured twice on two consecutive days by the same observer on both study occasions. The technical error of measurement (TEM) (absolute and relative value), and the coefficient of reliability (R) were calculated and compared across reliability studies. RESULTS: According to the R and relative TEM, inter-observer reliabilities were only acceptable for height and weight. In terms of intra-observer precision, while the first and second anthropometrists demonstrated better reliability than the third, only height and weight measurements were reliable. Looking at total TEM, we observed that while measurements remained stable between studies for height and weight, circumferences and skinfolds lost precision from one occasion to the next. CONCLUSIONS: Height and weight were the most reliable measurements under the study's conditions. Circumferences and skinfolds demonstrated less reliability and lost precision over time, probably as a result of insufficient supervision over the entire length of the study. Our results underline the importance of a careful observer's selection, good initial preparation, as well as the necessity of ongoing training and supervision over the entire course of a longitudinal nutritional study. Failure to do so could have major repercussions on data reliability and jeopardize its utilization.
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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 | 0.024 | 0.205 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 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".