Prevalence of underweight, wasting and stunting among young children with a significant cognitive delay in 47 low‐income and middle‐income countries
Bibliographic record
Abstract
BACKGROUND: Undernutrition in early childhood is associated with a range of negative outcomes across the lifespan. Little is known about the prevalence of exposure to undernutrition among young children with significant cognitive delay. METHOD: Secondary analysis of data collected on 161 188 three- and four-year-old children in 47 low-income and middle-income countries in Rounds 4-6 of UNICEF's Multiple Indicator Cluster Surveys. Of these, 12.3% (95% confidence interval 11.8-12.8%) showed evidence of significant cognitive delay. RESULTS: In both middle-income and low-income countries, significant cognitive delay was associated with an increased prevalence of exposure to three indicators of undernutrition (underweight, wasting and stunting). Overall, children with significant cognitive delay were more than twice as likely than their peers to be exposed to severe underweight, severe wasting and severe stunting. Among children with significant cognitive delay (and after controlling for country economic classification group), relative household wealth was the strongest and most consistent predictor of exposure to undernutrition. CONCLUSIONS: Given that undernutrition in early childhood is associated with a range of negative outcomes in later life, it is possible that undernutrition in early childhood may play an important role in accounting for health inequalities and inequities experienced by people with significant cognitive delay in low-income and middle-income countries.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".