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Record W2770432623 · doi:10.17925/use.2017.13.02.51

Type 1 Diabetes in Athletes

2017· article· en· W2770432623 on OpenAlexaff
Jane E. Yardley

Bibliographic record

VenueUS Endocrinology · 2017
Typearticle
Languageen
FieldMedicine
TopicDiabetes Management and Research
Canadian institutionsMedtronic (Canada)Abbott (Canada)Diabetes CanadaUniversity of Alberta
Fundersnot available
KeywordsMedicineAthletesType 2 diabetesDiabetes mellitusPhysical therapyDuration (music)Endocrinology

Abstract

fetched live from OpenAlex

Managing training and competition with type 1 diabetes is complicated, but not impossible. Athletes, and their diabetes care providers, should be aware of everything that might impact blood glucose responses to exercise. These factors include the type, timing, intensity and duration of exercise, as well as the timing and size of insulin dosage adjustments and carbohydrate intake. Due to differences in physiology (age, sex, fitness levels, etc.) there is no “one size fits all” solution, however, with careful planning and record-keeping, athletes with type 1 diabetes have the same opportunities for success in their sport as everyone else.

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 score0.124
Threshold uncertainty score0.982

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)0.0000.001

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 head0.038
GPT teacher head0.333
Teacher spread0.295 · 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
Published2017
Admission routes1
Has abstractyes

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