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Record W4414030222 · doi:10.1016/j.cdnut.2025.107547

Unbalanced Macronutrient Intakes, Multiple Micronutrient Inadequacies, and Diarrhea Underscore Low-Height-for-Age in Indigenous Panamanian Preschool Children

2025· article· en· W4414030222 on OpenAlexafffund
Marilyn E. Scott, Dorian Irwin Kristmanson, Eduardo Ortega‐Barría, Kristine G. Koski

Bibliographic record

VenueCurrent Developments in Nutrition · 2025
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsSte. Anne's HospitalMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMicronutrientIndigenousDiarrheaEnvironmental healthMedicinePediatricsBiologyInternal medicine

Abstract

fetched live from OpenAlex

Background: may have seasonally distinct contributions to high rates of stunting in preschool children. Objectives: -scores (HAZ) during a dry and rainy season in Panama's Comarca Ngäbe-Buglé. Methods: This prospective community-based study collected sociodemographic and health data from 328, 12‒59-mo-old children in both the dry and rainy seasons. Diets were assessed in nonbreastfeeding children using a food frequency questionnaire and a 24-h recall during both seasons. Bivariate comparisons between stunted and nonstunted children and between the dry and rainy seasons were conducted. Stepwise linear regression models identified associations of sociodemographic status, infections, food groups, and estimated nutrient intakes with HAZ. Results: < 0.0001) revealed that servings of grains/cereals and fat were positively associated with HAZ, and fruit and total sugar intake and diarrhea were negatively associated with HAZ. Conclusions: The multifactorial nature of linear growth faltering in these preschool children differed by season. The negative impact of diarrhea emerged only in the rainy season, but the negative impact of sugar intake and the positive impact of fat intake emerged in both seasons.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.012
GPT teacher head0.273
Teacher spread0.261 · 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 teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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

Citations0
Published2025
Admission routes2
Has abstractyes

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