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Record W4361208311 · doi:10.1093/pch/pxac076

Cow’s milk alternatives for children with cow’s milk allergy and beyond

2023· article· en· W4361208311 on OpenAlexafffundabout
Brock A. Williams, Stephanie C. Erdle, Kelsey M Cochrane, Kirstin Emma Wingate, Kyla J. Hildebrand

Bibliographic record

VenuePaediatrics & Child Health · 2023
Typearticle
Languageen
FieldMedicine
TopicFood Allergy and Anaphylaxis Research
Canadian institutionsBC Children's HospitalUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsMilk allergyCow's milk allergyMedicineMicronutrientAllergyCLARITYFood allergyBiotechnologyBiologyImmunology

Abstract

fetched live from OpenAlex

Cow's milk allergy (CMA) is one of the most common food allergies in the first years of life, with worldwide prevalence estimated to range from 2% to 5%. While the majority of children with CMA will eventually develop tolerance to cow's milk proteins (it is estimated that >75% of children with CMA develop tolerance to cow's milk proteins by the age of 3 years, and >90% develop tolerance by the age of 6 years), the selection of an appropriate cow's milk (CM) alternative for those with CMA is vital to ensure adequate growth and development during childhood. The increasing number of CM alternative products on the commercial market with markedly different nutritional content and micronutrient fortification adds a layer of complexity that can be challenging for both families and clinicians to navigate. This article aims to provide guidance and clarity to Canadian paediatricians and primary care clinicians on recommending the most appropriate, safe, and nutritionally optimal CM alternatives for individuals with CMA, and beyond.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.024
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.020
GPT teacher head0.309
Teacher spread0.289 · 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
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

Citations7
Published2023
Admission routes3
Has abstractyes

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