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Record W4394177359 · doi:10.6084/m9.figshare.11931552

Folate content of gluten-free food purchases and dietary intake are low in children with coeliac disease

2020· dataset· en· W4394177359 on OpenAlexaboutno aff
Samantha Cyrkot, Sven Anders, Chelsea Kamprath, Amanda Liu, Heather Mileski, Jenna Dowhaniuk, Roseann Nasser, Margaret Marcon, Herbert Brill, Justine Turner, Diana R. Mager

Bibliographic record

VenueFigshare · 2020
Typedataset
Languageen
FieldMedicine
TopicCeliac Disease Research and Management
Canadian institutionsnot available
Fundersnot available
KeywordsGluten freeCoeliac diseaseFood scienceGlutenMedicineDiseaseEnvironmental healthBiologyInternal medicine

Abstract

fetched live from OpenAlex

The lack of mandated folate enrichment of gluten-free (GF) grains in Canada has been suspected to contribute to suboptimal folate intake among children suffering from Celiac disease (CD). Children with CD on the gluten-free diet (GFD) face nutrient imbalances (higher fat/sugar, lower folate) from processed GF foods. The study objective examined folate intake in children with CD and folate content of household food purchases. Households collected food receipts for 30 days to assess folate content. Folate-rich foods were defined as ≥60 µg dietary folate equivalent (DFE)/100g. Two 24-hour recalls assessed children’s intake. Households (n = 73) purchased >17,000 food items. Median child age was 10.5 y (IQR: 8.4–14.1). GF folate-rich foods represented <15% of all household food purchases and 69% of children had low folate intakes. Folate-rich foods consumed included legumes/GF-breakfast cereals. These represented 5% of GF-food purchases/intake. Few were fortified with folate. Findings highlight the need for mandated GF folate food fortification policy.

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)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.552
Threshold uncertainty score0.902

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.007
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.003

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 head0.056
GPT teacher head0.266
Teacher spread0.210 · 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 designNot applicable
Domainnot available
GenreDataset

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
Published2020
Admission routes1
Has abstractyes

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