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Record W4413116166 · doi:10.1136/bmjgh-2024-016878

Infants and children 6–59 months of age with severe wasting and/or nutritional oedema: evidence gaps identified during WHO guideline development

2025· article· en· W4413116166 on OpenAlexaff
Debbie Thompson, Praveen Kumar, Aida H. Al-Sadeeq, Rozina Khalid, Hedwig Deconinck, James A. Berkley, Robert Bandsma, Nicky Dent, Indi Trehan, Marko Kerac, Celeste Naude, Allison I Daniel

Bibliographic record

VenueBMJ Global Health · 2025
Typearticle
Languageen
FieldMedicine
TopicChild Nutrition and Feeding Issues
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
FundersWorld Health Organization
KeywordsWastingGuidelineMedicinePediatricsIntensive care medicineInternal medicinePathology

Abstract

fetched live from OpenAlex

Wasting describes a condition where a child is dangerously thin for their height, typically due to sudden and severe weight loss. It greatly raises the risk of death but can be effectively treated. In 2024, an estimated 42.8 million infants and children under 5 years of age were affected by wasting at any given time, and, of these, 12.2 million were severely wasted. 1 Nutritional oedema is not captured in these estimates, yet there are likely hundreds of thousands of children with this form of malnutrition.2 3

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.055
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.985
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.055
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.004
Bibliometrics0.0030.004
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0020.002
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0070.002

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.

Opus teacher head0.029
GPT teacher head0.378
Teacher spread0.349 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designSystematic review
DomainMethods
GenreReview

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".

Quick stats

Citations5
Published2025
Admission routes1
Has abstractyes

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