393-P: Technology Use and Hypoglycemia in Type 1 Diabetes: Insights from the BETTER Registry
Bibliographic record
Abstract
The BETTER registry launched in April 2019 aims to understand the burden (frequency, severity) of hypoglycemia from people with T1D’s perspective and to evaluate the role of technologies in its prevention and management. Methods: BETTER registry includes several online surveys and is being conducted in Quebec, Canada. Preliminary results of the 2ndsurvey about technology use, CSII and FGM/CGM, in relation to hypoglycemia are reported. Results: By Dec 2019, 457 persons with T1D (> 14 y.o.) have completed the 2ndsurvey (44.0±15.7 y.o., 67.2 % females, 48.0% CSII users, 80% FGM/CGM users in past 12 months). Main reasons for opting for technology: A) CSII (n=219): glucose control with exercise (72.6%), elevated HbA1c (42.9%), nocturnal hypoglycemia (35.2%) as well as overall hypoglycemia severity and frequency (26.0%). Interestingly, 65.3% reported less hypoglycemia after adopting CSII. B) FGM/CGM (n=365): easier to follow glucose levels (77.5%), stop/reduce capillary measurements (69%), easier glucose control with exercise (68.5%). Table1 shows comparisons between technology users vs. non users: CSII and FGM/CGM users reported better glucose control, more hypoglycemia episodes (probably due to better diagnosis) that are corrected at higher BG levels. Conclusion: This database will improve the understanding of technologies roles in hypoglycemia detection, prevention and treatment to optimize their use. Disclosure N. Taleb: None. K. Desjardins: None. S. Haag: None. M. Prevost: 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)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.012 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".