Nothing about us without us: a priority-setting partnership for research in type 1 diabetes and exercise
Bibliographic record
Abstract
Background: Engaging patients in health research can align study questions and research designs with relevant priorities. Priorities for exercise-related research for persons living with type 1 diabetes (T1D) remain unclear. Methods: Individuals with lived experience of T1D were engaged in a modified James Lind Alliance process to identify research priorities in T1D and exercise. An online survey gathered research questions from patients, caregivers and healthcare providers across Canada. Submissions were qualitatively analyzed and the resulting long-list was distributed to a 12-person stakeholder steering committee. Members individually ranked their top ten questions and rankings were collated to create a short-list, discussed in a final workshop. Results: 115 individuals across Canada completed the survey, yielding 194 research questions. After qualitative analysis, 38 questions were long-listed and following committee ranking, 24 questions were short-listed for workshop discussions. The top 10 were: (1) What explains the individual variation in the response to exercise?; (2) Which is the best for maintaining glycemic stability and glucose tolerance: aerobic training, strength training, or a combination of both? If a combination, does the order matter?; (3) What modes of exercise produce the best health benefits while maintaining tight glycemic control?; (4) What dietary plans can safely and effectively be followed for an active lifestyle in type 1 diabetes without compromising pre- and post-exercise glycemic control?; (5) What is the optimal time of day and exercise prescription (example: how often, what type, how intense) in order to maintain ideal glycemic control and insulin sensitivity?; (6) What is the best method of preventing post-exercise hypo- or hyperglycemia?; (7) Will certain glycemic ranges before starting exercise consistently result in hypo- or hyperglycemia?; (8) What effect can various levels of hydration have on blood sugar levels during and after exercise?; (9) How does hypo- or hyperglycemia affect muscle growth and strength training progress, or vice versa?; and (10) What is the effect of climate/temperature on blood sugar control during exercise and what causes this effect?. Conclusion: This list could inform future iterations of exercise studies for persons with T1D. The prioritized questions focused on lifestyle differences, optimal glucose control and performance progression.
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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 | 0.180 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.032 | 0.014 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.006 | 0.039 |
| Research integrity | 0.007 | 0.020 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".