Adolescents With Type 1 Diabetes Using Dapagliflozin as an Adjunct-to-Insulin: Perspectives on Experiences of Clinical Trial Participation
Bibliographic record
Abstract
OBJECTIVES: The Adolescent Type 1 diabetes Treatment with SGLT2i for hyperglycEMia & hyPerfilTration (ATTEMPT) study evaluated the impact of sodium-glucose cotransporter-2 inhibitors (SGLT2i) as an adjunct-to-insulin therapy. Adolescent experiences were explored to gain their informed perspective on adjunct-to-insulin medications as a potential therapeutic intervention in pediatric diabetes and clinical trial protocol. METHODS: Within an embedded experimental mixed method design, a qualitative description approach was used with content and thematic analysis to explore emergent findings. RESULTS: A total of 24 adolescents (n = 13 males), ages 12 to 17 years, participated in semi-structured interviews. Insights were gained from their shared experience, comprising 3 themes describing how: (1) decision-making about trial participation was multifaceted, (2) adhering to an SGLT2i adjunctive medication was non-burdensome, and (3) participating in the trial had unexpected informative benefits, including improved self-management skills and ketone measurement awareness. CONCLUSIONS: Adolescents in the ATTEMPT study did not express a higher burden of daily diabetes management with adjunct-to-insulin therapy. Findings support the value of a strong research team to ensure communication with adolescents with attention to ethical considerations around recruitment, consent, and participation. Adolescent perspectives in efficacy studies of new treatments in type 1 diabetes can optimize trial designs, research validity, and inform outcome findings.
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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 | 0.044 | 0.091 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".