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Record W2770510870 · doi:10.3390/nu9111266

Evaluation of Dietary Intakes and Supplement Use in Paralympic Athletes

2017· article· en· W2770510870 on OpenAlexafffund
Robyn F. Madden, Jane Shearer, Jill A. Parnell

Bibliographic record

VenueNutrients · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsMount Royal UniversityUniversity of Calgary
FundersMount Royal University
KeywordsAthletesDietary Reference IntakeMicronutrientReference Daily IntakePantothenic acidMedicineVitaminEnvironmental healthPhysical therapyNutrientBiologyEndocrinology

Abstract

fetched live from OpenAlex

Dietary intakes and supplement use in Paralympic athletes remains largely unexplored, and specialized recommendations are lacking. The aim of this study was to evaluate nutrient intakes and supplement use in high-performance athletes with physical disabilities using three-day food records and a validated dietary supplement use questionnaire. A secondary aim examined gender differences in nutrient and supplement intakes. Male (n = 18) and female (n = 22) athletes were recruited from nine Paralympic sports through sporting organizations, coaches, and social media. Athletes generally met able-bodied recommendations for macronutrients. Male and female athletes often failed to meet the Recommended Dietary Allowance (RDA) or Adequate Intake (AI) for vitamin D, vitamin E, pantothenic acid, magnesium, and potassium. On average, females did not meet the RDA for iron and calcium, whereas males did not meet the RDA for vitamin A and folate. Commonly consumed supplements were vitamin D, protein powder, sport bars, and sport drinks. Analysis of diet and supplement use within this population shows several micronutrient deficiencies and irregular use of specific supplements. Athlete support and education is required to optimize nutrition in Paralympic athletes.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.167
Threshold uncertainty score0.220

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.062
GPT teacher head0.321
Teacher spread0.259 · 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

Citations50
Published2017
Admission routes2
Has abstractyes

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