397-P: BETTER Understanding Hypoglycemia in Type 1 Diabetes (T1D)
Bibliographic record
Abstract
Introduction: For patients living with T1D, hypoglycemia is the most frequent complication and obstacle in achieving optimal glycemic control with potentially significant adverse implications on individuals’ quality of life. To improve hypoglycemia prevention and management, the BETTER registry for people living with T1D was launched in April 2019 in the province of Quebec, Canada. Methods: Participants self-register online in the BETTER registry and are asked to answer a series of questionnaires (3 phases). We report the preliminary results of phase I among participants of ≥14 years old, which aims to provide a comprehensive portrait of the population with T1D. Results: Mid-December 2019, 718 individuals (64.6% female, age 41.8±15.4 years old, duration of diabetes 22.4±14.7 years, 93.2% with T1D and 6.7% with LADA, 71.2% with HbA1c <8%) have registered. Of the participants, 99.6% were currently on insulin therapy (46.7% on insulin pumps, 51.2% on insulin injections, 1.7% using both), and 77.3% were using continuous or flash glucose monitoring. They reported, on average, 6.27±7.42 episodes of hypoglycemia (<54 mg/dl) in the last month. Since their diagnosis, 47.8% of participants had at least one episode of severe hypoglycemia (SH: requiring help from another person or use of glucagon or hospitalization or loss of consciousness). Among those who reported SH in the last 12 months (n=91), the average number of episodes was 2.34±3.37 during this period. When asked what hypoglycemia represents using one word, one-fifth of participants used the word “weak” or “weakness." Conclusion: In a contemporary group of patients widely using technology for T1D management, hypoglycemia remains frequent. The growing BETTER registry will allow further understanding of this challenging reality and enable future research aiming to reduce hypoglycemia episodes. Disclosure R. Cheng: None. N. Taleb: None. V. Messier: Other Relationship; Self; Eli Lilly and Company. M.K. Talbo: None. A. Roy-Fleming: None. R. Rabasa-Lhoret: Advisory Panel; Self; AstraZeneca, Eli Lilly and Company, Insulet Corporation, Janssen Pharmaceuticals, Inc. Board Member; Self; Merck & Co., Inc., Sanofi. Research Support; Self; Eli Lilly and Company, Novo Nordisk A/S, Novo Nordisk A/S, Sanofi. A. Brazeau: None. Funding Canadian Institutes of Health Research (JT1-157204); JDRF (4-SRA-2018-651-Q-R)
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".