Deworming children for soil-transmitted helminths in low and middle-income countries: systematic review and individual participant data network meta-analysis
Bibliographic record
Abstract
Intestinal parasites affect millions of children globally. We aimed to assess effects of deworming children on nutritional and cognitive outcomes across potential effect modifiers using individual participant data (IPD). We searched multiple databases to 27 March 2018, grey literature, and other sources. We included randomised and quasi randomised trials of deworming compared to placebo or other nutritional interventions with data on baseline infection. We used a random-effects network meta-analysis with IPD and assessed overall quality, following a pre-specified protocol. We received IPD from 19 trials of STH deworming. Overall risk of bias was low. There were no statistically significant subgroup effects across age, sex, nutritional status or infection intensity for each type of STH. These analyses showed that children with moderate or heavy intensity infections, deworming for STH may increase weight gain (very low certainty). The added value of this review is an exploration of effects on growth and cognition in children with moderate to heavy infections as well as replicating prior systematic review results of small effects at the population level. Policy implications are that complementary public health strategies need to be assessed and considered to achieve growth and cognition benefits for children in helminth endemic areas.
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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 | 0.022 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.027 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".