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Record W2516472775 · doi:10.1249/jsr.0000000000000282

Commercial Hype Versus Reality: Our Current Scientific Understanding of Gluten and Athletic Performance

2016· review· en· W2516472775 on OpenAlexaff
Dana M. Lis, JW Fell, Kdk Ahuja, Cecilia M. Kitic, Trent Stellingwerff

Bibliographic record

VenueCurrent Sports Medicine Reports · 2016
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsCanadian Sport Centre Pacific
Fundersnot available
KeywordsAthletesMedicinePsychosocialGlutenIntestinal permeabilityGluten freePopulationConfoundingEnvironmental healthIntensive care medicinePhysical therapyInternal medicinePsychiatryPathology

Abstract

fetched live from OpenAlex

Recent explosion in the prevalence of gluten-free athletes, exacerbated by unsubstantiated commercial health claims, has led to some professional athletes touting gluten-free diet as the secret to their success. Forty-one percent of athletes report adhering to a gluten-free diet (GFD), which is four-fold higher than the population-based clinical requirements. Many nonceliac athletes believe that gluten avoidance improves gastrointestinal well-being, reduces inflammation, and provides an ergogenic edge, despite the fact that limited data yet exist to support any of these benefits. There are several plausible associations between endurance-based exercise and gastrointestinal permeability whereby a GFD may be beneficial. However, the implications of confounding factors, including the risks of unnecessary dietary restriction, financial burden, food availability, psychosocial implications, alterations in short-chain carbohydrates (fermentable oligosaccharides, disaccharides, monosaccharides, and polyols), and other wheat constituents emphasize the need for further evaluation.

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)0.002
metaresearch head score (Gemma)0.004
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: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.003
Science and technology studies0.0010.003
Scholarly communication0.0030.005
Open science0.0010.001
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0030.001

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 head0.159
GPT teacher head0.390
Teacher spread0.232 · 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
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

Citations18
Published2016
Admission routes1
Has abstractyes

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