<scp>FRONTIER</scp> : <scp>FReeStyle</scp> Libre system use in Ontario among people with diabetes in the <scp>IC</scp> / <scp>ES</scp> database—Evidence from real‐world practice: Patients on basal insulin, glucagon‐like peptide 1 receptor agonist or oral therapies
Bibliographic record
Abstract
AIM: We aimed to investigate glycated haemoglobin (HbA1c) levels and healthcare resource utilization (HCRU; emergency department [ED] visits or hospitalization) before and after adoption of FreeStyle Libre sensor-based glucose monitoring systems (FSL) by people with type 2 diabetes mellitus (T2DM) on basal insulin without glucagon-like peptide 1 receptor agonist (GLP-1 RA) therapy, basal insulin with GLP-1 RA therapy, GLP-1 RA therapy without insulin or oral therapy alone. MATERIALS AND METHODS: Routinely collected administrative health data (housed at IC/ES, formerly the Institute for Clinical Evaluative Sciences) in Ontario, Canada were used to identify 20 253 people with T2DM who had a first FSL claim between 16 September 2019 and 31 August 2020 (index date) and remained active on FSL for 24 months' follow-up. HCRU was measured for 12 months before the index date and the last 12 months of the 24-month follow-up period. HbA1c data were taken from the latest tests in each period. RESULTS: Mean HbA1c was statistically significantly reduced after FSL acquisition among people aged ≤65 or >65 years in all four treatment groups (range, 0.3-0.8% reduction). After FSL acquisition, ED visits and hospitalization were statistically significantly reduced in the oral therapy only group and in some basal insulin subgroups (without GLP-1 RA, all except hospitalization aged ≤65 years; with GLP-1 RA, only ED visits aged ≤65 years). CONCLUSIONS: Among people with T2DM using basal insulin and/or non-insulin therapies, HbA1c levels were statistically significantly improved and HCRU was reduced after initiation of FSL.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".