375-P: Comparison of Blood Glucose Values Used for Nonsevere Hypoglycemia Management with Corresponding Continuous Glucose Monitoring Data in Type 1 Diabetes—A Secondary Analysis of the REVERSIBLE Trial
Bibliographic record
Abstract
Background: Guidelines recommend treating non-severe (NS) hypoglycemia with 15g carbohydrates (CHO) at 15minutes intervals when blood glucose (BG) falls below 72 mg/dL. No specific recommendations exist for people living with type 1 diabetes (PwTID) using continuous glucose monitoring (CGM) despite known time delays between BG and CGM values. Objective: to assess CGM data corresponding to BG values used for NS hypoglycemia management. Methods: Secondary analysis of the REVERSIBLE trial (accepted in Diabetes Care); three arms open-label crossover study assessing the efficacy of 16g oral CHO for preventing NS hypoglycemia (insulin induced) at different BG levels: <72 mg/dL (control using 15 rule), ≤80mg/dL or ≤90 mg/dL. CGM data (Dexcom G6® ) were compared to BG to assess mean absolute relative difference (MARD) at time of CHO intake. Results: Participants (n=29) were 52% male, 46.8±16.3 y.o., BMI 26.4±3.9 kg/m2, diabetes duration 26.2±15.9 years, A1c 53.4±15.5 mmol/mol and 62% insulin pump users. BG levels corresponded to higher Dexcom values for all arms at time of CHO intake (p<0.001). Participants with hypoglycemia %, BG vs. dexcom values (mean [95% CI]) and MARD were as follows: for <72 mg/dL arm, 100% participants, 64.0 [62.0-66.0] mg/dL vs. 85.5 [76.0-95.0] mg/dL, MARD 34.4 [18.1-50.6] %; for ≤80 mg/dl arm, 86% participants, 72.7 [70.4-74.9] vs. 93.1 [84.3-102.0] mg/dL, MARD 28.2 [17.1-39.3] % ; for ≤90 mg/dl arm, 34% participants; 82.8 [81.2- 84.4] vs. 99.5 [93.6-105.3] mg/dL, MARD 20.3 [13.4-27.1]. No significant rebound hyperglycemia (BG>180 mg/dl) was observed within the first hour for all arms. Conclusion: When using CGM, PwTID may need to treat NS hypoglycemia at a higher threshold than the 72mg/dl without increasing rebound hyperglycemia risks. Research is warranted to determine optimal CGM level for management and prevention of NS hypoglycemia in free living settings. Disclosure S. Al-Mahayni: None. R. Cheng: Other Relationship; Novo Nordisk. Z. Wu: None. V. Messier: None. 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. 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. N. Taleb: Other Relationship; Dexcom, Inc. Consultant; Viatris Inc. Other Relationship; Novo Nordisk. Funding Secondary analysis (primary study supported by Funding: Juvenile Diabetes Research Foundation (4-SRA-2018-651-Q-R) and CIHR/SPOR (JT1-157204).
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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.
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".