Validation of MUAC Cut-Offs of WHO for Diagnosis of Acute Malnutrition among Children under 5 Years in Karachi, Pakistan
Bibliographic record
Abstract
Objective: To validate the WHO recommended Mid-Upper Arm Circumference (MUAC) cut-offs for acute malnutrition screening in children younger than five in Karachi, Pakistan. Methods: A cross-sectional study was conducted, including an anthropometric examination following WHO guidelines. Height was measured using Stadiometer and Infantometer. The link between MUAC and Weight-for-Height-Z score (WHZ) for different cut-offs of MUAC for Moderate Acute Malnutrition (MAM) and Severe Acute Malnutrition (SAM) was shown using Receiver Operator Characteristics (ROC) curves and the Youden index. Sensitivity and specificity of MUAC <11.5 cm and ≥11.5 to <12.5cm were determined using WHZ scores of -3 Standard Deviation (SD) and ≥-3 to <-2 SD for SAM and MAM, respectively. Results: Among 499 children, as per WHZ score, 9.6% and 27.1% had SAM and MAM, respectively, whereas according to MUAC, 6.4% and 3.6% had MAM and SAM, respectively. At the maximum value of the Youden index of 55.6%, an optimum cut-off of 12.7cm for screening of SAM with MUAC was found compared to the recommended cut-off of 11.5cm. Similarly, at the maximum value of the Youden index of 57.7%, an optimum cut-off of 13.9cm for screening of MAM with MUAC was found compared to the recommended cut-off of 12.5cm. Conclusion: The current MUAC cut-off of WHO for screening SAM and MAM cases captures only a small percentage of children under five. This needs to be revised to capture children with acute malnutrition for timely treatment in Pakistan.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".