Systematic Review of Management of Moderate Wasting in Children over 6 Months of Age
Bibliographic record
Abstract
The effective management of the 33 million children with moderate acute malnutrition (MAM) is key to reducing childhood morbidity and mortality. In this review, we aim to evaluate the effectiveness of specially formulated foods (SFFs) compared to non-food-based approaches to manage MAM in children >6 months old. We conducted a search on ten databases until 23 August 2021 and included five studies, covering 3387 participants. Meta-analysis of four studies comparing SFFs to counselling or standard of care showed that SFFs likely increase recovery rate, reduce non-response, and may improve weight-for-height z-score, weight-for-age z-score and time to recovery, but have little or no effect on MUAC gain. One study on a multicomponent intervention (SFFs, antibiotics and counselling provided to high-risk MAM) compared to counselling only was reported narratively. The intervention may increase weight gain after 24 weeks but may have little or no effect on weight gain after 12 weeks and on non-response and mortality after 12 and 24 weeks of enrollment. The effect of this intervention on recovery was uncertain. In conclusion, SFFs may be beneficial for children with moderate wasting in humanitarian contexts. Programmatic recommendations should consider context and cost-effectiveness.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".