Peak performance: Putting type 1 diabetes management recommendations for athletes to the test
Bibliographic record
Abstract
Background: Athletes with type 1 diabetes (T1D) face unique challenges to maintain optimal glucose levels and therefore require tailored guidance from their healthcare providers. Herein, we aim to summarize and compare recommendations targeted at T1D management in athletes in commonly used clinical practice guidelines and topical position statements. The objective is to assess if the available recommendations are comprehensive enough for athletes to apply to high-performance sport. Methods: From seven clinical practice guidelines and positions statements, we identified recommendations relevant to athletes with T1D, based on a specific hierarchy. For included recommendations, we extracted relevant information including the year of publication, author(s), chapter name or number, text for the recommendation, and level of evidence. Based, on the clinical topic covered, we grouped included recommendations to five themes. Results: = 18). The 2018 Diabetes Canada and 2021 American Diabetic Association guidelines linked recommendations directly with levels and grades of evidence. None of the recommendations had level 1 or grade A evidence. Three recommendations from Diabetes Canada reported level 2, grade B evidence. American Diabetic Association reported 1 recommendation with grade B evidence, and 2 recommendations with grade C evidence. Conclusions: There is an opportunity for expansion of clinical practice guidelines to increase the depth and breadth of recommendations for high performance athletes with T1D.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.039 | 0.182 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".