Patterns of metformin use and glycated haemoglobin trends among patients with newly diagnosed type 2 diabetes in Alberta, Canada
Bibliographic record
Abstract
AIM: Canadian guidelines recommend metformin as first-line therapy for incident uncomplicated type 2 diabetes and the vast majority of patients are treated accordingly. However, only 54% 65% remain on treatment after 1 year, with the highest discontinuation rates within the first 3 months. The purpose of this study was: (a) to identify individual and clinical factors associated with metformin discontinuation among patients with newly diagnosed uncomplicated type 2 diabetes in Alberta, Canada, and (b) describe glycated haemoglobin (HbA1c) trajectories in the first 12 months after initiation of pharmacotherapy, stratified by metformin usage pattern. MATERIALS AND METHODS: We conducted a retrospective cohort study using linked administrative datasets from 2012 to 2017 to define a cohort of individuals with uncomplicated incident type 2 diabetes. Using logistic regression, we determined individual and clinical characteristics associated with metformin discontinuation. We categorized individuals based on patterns of metformin use and then used mean HbA1c measurements over a 12-month follow-up period to determine glycaemic trajectories for each pattern. RESULTS: Characteristics associated with metformin discontinuation were younger age, lower baseline HbA1c and having fewer comorbidities. Sex, income and location (urban/rural) were not significantly associated with metformin discontinuation. Individuals who continued metformin with higher adherence and individuals who discontinued metformin entirely had lowest HbA1c values at 12 months from treatment initiation. Those who changed therapy or had additional therapies added had higher HbA1c values at 12 months. CONCLUSION: Identifying characteristics associated with discontinuation of metformin and individuals' medication usage patterns provide an opportunity for targeted interventions to support patients' glycaemic management.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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".