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Record W4391768388 · doi:10.1093/nutrit/nuad165

Micronutrient status in children aged 6–59 months with severe wasting and/or nutritional edema: implications for nutritional rehabilitation formulations

2023· review· en· W4391768388 on OpenAlexaff
Laura Vresk, Mary Flanagan, Allison I Daniel, Isabel Potani, Céline Bourdon, Carolyn Spiegel-Feld, Mehakpreet Thind, Amber Farooqui, Catriona Ling, E. Miraglia, Guanlan Hu, Bijun Wen, Stanley Zlotkin, Philip James, Marie McGrath, Robert Bandsma

Bibliographic record

VenueNutrition Reviews · 2023
Typereview
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsUniversity of TorontoHospital for Sick Children
Fundersnot available
KeywordsWastingMicronutrientMalnutritionMedicineMicronutrient deficiencyWasting SyndromeEtiologySevere Acute MalnutritionIntensive care medicineEdemaPediatricsEnvironmental healthSurgeryInternal medicinePathology

Abstract

fetched live from OpenAlex

Undernutrition remains a global struggle and is associated with almost 45% of deaths in children younger than 5 years. Despite advances in management of severe wasting (though less so for nutritional edema), full and sustained recovery remains elusive. Children with severe wasting and/or nutritional edema (also commonly referred to as severe acute malnutrition and part of the umbrella term "severe malnutrition") continue to have a high mortality rate. This suggests a likely multifactorial etiology that may include micronutrient deficiency. Micronutrients are currently provided in therapeutic foods at levels based on expert opinion, with few supportive studies of high quality having been conducted. This narrative review looks at the knowledge base on micronutrient deficiencies in children aged 6-59 months who have severe wasting and/or nutritional edema, in addition to highlighting areas where further research is warranted (See "Future Directions" section).

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.061
GPT teacher head0.362
Teacher spread0.301 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
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

Citations7
Published2023
Admission routes1
Has abstractyes

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