919-P: Canada Real-World Analysis of Flash Glucose Monitoring and Impact on Time-in-Range and Hypoglycemia
Bibliographic record
Abstract
Prior analyses of global data from real world use of flash glucose monitoring have associated frequency of scanning with greater time in range and lower mean glucose, glucose variability, and hypoglycemia. These analyses did not include data from North America as flash glucose monitoring became available in late 2017. The objective of this follow-up analysis is to focus on real world outcomes in patients using flash glucose monitoring in Canada. A server collected anonymized data from patients whose flash glucose readers are uploaded (by patient or health care professional). Data from patients within Canada was analyzed through September 2018 (approximately 1 year). To understand the relationship between time in range and glucose variability with monitoring frequency, individuals were divided into 10 equal sized groups based on scanning frequency. Average ± SE time in range (glucose 70 mg/dL - 180 mg/dL [3.9 mmol/L - 10.0 mmol/L]) and time in glucose ≤ 54 mg/dL (∼3.0 mmol/L) was calculated for each group. This analysis includes 15,424 readers, 95,103 sensors, and 108 million glucose measurements with an average of 12 scans per day. Patients in the lowest scanning frequency decile (3.7 scans per day) spent 12.6 ± 0.14 hours in range and 28.8 ± 1.2 minutes with a glucose ≤ 54 mg/dL. Patients in the highest scanning frequency decile (29.2 scans per day) spent 16.6 ± 0.12 hours in range and 22.1 ± 1.0 minutes with a glucose ≤ 54 mg/dL. Real-world data from Canada demonstrates that higher frequency of scanning is associated with increased time in range and decreased hypoglycemia. This analysis is consistent with prior analyses and suggests that patients with diabetes who scan to obtain their glucose more frequently derive greater benefit than patients who scan less frequently. Disclosure L. Berard: Advisory Panel; Self; Eli Lilly and Company. Consultant; Self; Abbott, Ascensia Diabetes Care, AstraZeneca, Bayer AG, Becton, Dickinson and Company, Janssen Pharmaceuticals, Inc., LifeScan Canada, Mylan, Novo Nordisk Inc., Sanofi. Research Support; Self; Montmed Inc. Speaker's Bureau; Self; Boehringer Ingelheim Pharmaceuticals, Inc., Merck & Co., Inc. N. Virdi: Employee; Self; Abbott, Proteus Digital Health. Stock/Shareholder; Self; Johnson & Johnson. T. Dunn: Employee; Self; Abbott Laboratories.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".