680-P: Effect of the FreeStyle Libre System on Diabetes Treatment for People with T2D: Results from a Retrospective Cohort Study Using Canadian Private Payer Claims Database
Bibliographic record
Abstract
Introduction: Therapeutic inertia is a major contributor to people with diabetes not achieving glycemic goals. We assessed for patients with T2D the impact of using the FreeStyle Libre system (FSL) vs. blood glucose monitoring (BGM) on treatment intensification. Method: We carried out a matched retrospective cohort study using secondary private payer claims data including >30 million diabetes drug and device claims filled by over 850,000 patients with T2D >18 in Canada over 24 months. Each month, patients were classified by level of therapy progression: 1. No diabetes drug therapy; 2. Mono Oral Antihyperglycemic Agents (OHAs) ; 3. Dual OHAs; 4. Triple OHAs; 5. Quad or more OHAs; 6. Injectable GLP1-RA (±concomitant OHAs) ; 7. Basal insulin (±concomitant OHAs) ; 8. MDI insulin (±concomitant OHAs) . Results: A total of 373,871 patients met the inclusion criteria. Across all treatment cohorts, the FSL treatment groups were found to have a statistically higher probability of treatment intensification relative to BGM: Conclusion: Reimbursement of FSL for patients with T2D in Canada is associated with decreased time lag for glucose lowering therapy compared to those using BGM alone. These findings suggest that FSL data impact clinicians to facilitate earlier and more intensive therapy modifications and thus reduce treatment inertia. Disclosure S.B.Harris: Consultant; Abbott, AstraZeneca, Eli Lilly and Company, Novo Nordisk, Sanofi, Other Relationship; Abbott, AstraZeneca, Bayer Inc., Dexcom, Eli Lilly and Company, HLS Therapeutics, Janssen Pharmaceuticals, Inc., Novo Nordisk, Sanofi, Research Support; Applied Therapeutics Inc., AstraZeneca, Canadian Institutes of Health Research, Juvenile Diabetes Research Foundation (JDRF) , Novo Nordisk, Sanofi, The Lawson Foundation. F.Levrat-guillen: Employee; Abbott. Funding Funded by Abbott Diabetes Care
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".