Neurodevelopment and Recovery From Wasting
Bibliographic record
Abstract
Acute illness with malnutrition is a common indication for hospitalization among children in low- and middle-income countries. We investigated the association between wasting recovery trajectories and neurodevelopmental outcomes in young children 6 months after hospitalization for an acute illness.Children aged 2 to 23 months were enrolled in a prospective observational cohort of the Childhood Acute Illness & Nutrition Network, in Uganda, Malawi, and Pakistan between January 2017 and January 2019. We grouped children on the basis of their wasting recovery trajectories using change in mid-upper arm circumference for age z-score. Neurodevelopment was assessed with the Malawi Developmental Assessment Tool (MDAT development-for-age z-score [DAZ]) at hospital discharge and after 6 months.We included 645 children at hospital discharge (mean age 12.3 months ± 5.5; 55% male); 262 (41%) with severe wasting, 134 (21%) with moderate wasting, and 249 (39%) without wasting. Four recovery trajectories were identified: high-stable, n = 112; wasted-improved, n = 404; severely wasted-greatly improved, n = 48; and severely wasted-not improved, n = 28. The children in the severely wasted-greatly improved group demonstrated a steep positive MDAT-DAZ recovery slope. This effect was most evident in children with both wasting and stunting (interaction wasted-improved × time × stunting: P < .001). After 6 months, the MDAT DAZ in children with wasting recovery did not differ from community children. In children who never recovered from wasting, there remained a significant delay in MDAT DAZ scores.Neurodevelopment recovery occurred in parallel with wasting recovery in children convalescing from acute illness and was influenced by stunting.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".