620-P: Snack-Related Insulin Adjustments in Type 1 Diabetes—A Delphi Consensus
Bibliographic record
Abstract
Introduction & Objective: Snacks are a part of normal eating patterns for many people, including those living with type 1 diabetes (T1D). However, the research on snacks and related insulin adjustments is limited. The aim of this Delphi study was to define a list of recommendations related to snack consumption for people living with T1D (pwT1D) based on lived and clinical experience. Methods: We surveyed Canadian clinicians and pwT1D on snack-time insulin adjustments. The Delphi method was used as a structured and iterative tool to obtain expert opinion and reach consensus over 3 rounds of questionnaires (1st open-ended followed by two close-ended questionnaires). Consensus was reached if ≥80% of participants agreed with a statement initially provided in the 1st round. Results: A total of 37 participants (21 pwT1D and 16 clinicians) completed all 3 phases. For snacks between-meal, consensus was reached supporting the need for an insulin dose if the snack contains >15g of carbohydrates (CHO) when using an automated insulin delivery (AID) and >20g of CHO with other treatment modalities. The insulin dose given for this snack should be reflective of the CHO content using insulin-to-CHO specific to the snack or the closest meal. When taking a snack at bedtime, participants agreed that an insulin dose would be needed only if the snack contains >20g of CHO. The dose should be calculated using a cautious ratio specific to bedtime. In both scenarios, between meals and at bedtime, no insulin should be given if the snack was taken in the context of hypoglycemia prevention or post hypoglycemia treatment, if the person is using intermediate acting or premixed insulins, if there is insulin on board, or if there are downward trend arrow on the continuous glucose monitor. Conclusion: Findings from this consensus provide a valuable reference for clinicians and people pwT1D to personalize inclusion of snacks and related insulin adjustments. This work also serves to inform the design of future trials testing the efficacy of proposed adjustments. Disclosure M.K. Talbo: None. A. Roy-Fleming: None. S. Haag: Other Relationship; Omnipod. C. Gosselin: None. T. Peters: None. J. Yale: Speaker's Bureau; Novo Nordisk Canada Inc., Abbott, Eli Lilly and Company. Advisory Panel; Novo Nordisk Canada Inc., Bayer Inc. Speaker's Bureau; Insulet Corporation. Advisory Panel; Eli Lilly and Company. Speaker's Bureau; Dexcom, Inc., Sanofi, Janssen Pharmaceuticals, Inc. Advisory Panel; Boehringer-Ingelheim, Mylan. Speaker's Bureau; Bayer Inc. Advisory Panel; Sanofi. Speaker's Bureau; Merck & Co., Inc. Research Support; Bayer Inc., Novartis Canada, Novo Nordisk Canada Inc. R.P.R. Rabasa-Lhoret: Other Relationship; Abbott, AstraZeneca, Bayer Inc., Boehringer-Ingelheim, Dexcom, Inc. Research Support; Diabetes Canada. Other Relationship; Eli Lilly and Company. Research Support; Cystic Fibrosis Canada, Canadian Institutes of Health Research, FFRD - Fondation Francophone pour la Recherche du Diabète. Other Relationship; Janssen Pharmaceuticals, Inc. Research Support; Juvenile Diabetes Research Foundation (JDRF). Other Relationship; Novo Nordisk, GlaxoSmithKline plc. Consultant; HLS Therapeutics Inc., Insulet Corporation. Speaker's Bureau; CPD Networks. Other Relationship; Medtronic. Consultant; Pfizer Inc. Speaker's Bureau; Tandem Diabetes Care, Inc. Other Relationship; Sanofi. Speaker's Bureau; Vertex Pharmaceuticals Incorporated. Research Support; SFD - Société Francophone du Diabète. A. Brazeau: Other Relationship; Dexcom, Inc. Research Support; Canadian Institutes of Health Research, Juvenile Diabetes Research Foundation (JDRF), Diabète québec, Fonds de recherche du Québec en Santé.
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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 | 0.106 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".