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Self‐reported lactose intolerance in Canadian adults

2012· article· en· W3176896178 on OpenAlexaffabout
Susan I. Barr

Bibliographic record

VenueThe FASEB Journal · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDigestive system and related health
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineLactose intoleranceVitamin D and neurologyPopulationEnvironmental healthAustralian populationFood frequency questionnaireLactoseFood scienceEndocrinology

Abstract

fetched live from OpenAlex

Little is known about the prevalence of self‐reported lactose intolerance (LI) in the Canadian population, and its implications for intakes of nutrients such as calcium and vitamin D. Accordingly, an on‐line survey was completed in September 2011 by a nationally‐representative sample of 2554 English‐ or French‐speaking adults aged ≥19 y. The survey included a semi‐quantitative food frequency questionnaire to assess intake of milk products and soy beverages, questions on use of calcium and vitamin D supplements, a scale to assess health beliefs toward milk products, and questions on LI. Overall, 16% of Canadian adults reported LI. This was significantly more common in women than men (20% vs 12%), in younger than older adults (18% vs 13%), and in non‐Caucasians than Caucasians (23% vs 15%), but did not vary by education level. Those with self‐reported LI had less positive beliefs about health benefits of milk products (mean ± SE score out of 5: 3.1 ± 0.05 vs 3.7 ± 0.01), were less likely to meet recommended intakes of milk products and alternatives (23% vs 41%), and were more likely to use supplemental calcium (52% vs 48%) and vitamin D (58% vs 42%). They had a higher prevalence of inadequate intakes of calcium (68% vs 58%) although vitamin D adequacy did not differ. Aspects of the results suggest the need for additional education on managing perceived LI. Supported by a grant from the Canadian Agri‐Science Cluster Initiative.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.230
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.009
GPT teacher head0.251
Teacher spread0.241 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2012
Admission routes2
Has abstractyes

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