Basal Insulin Initiation in Adults With Type 2 Diabetes Mellitus: A Retrospective Cohort Study Using Administrative Health Data in Alberta, Canada
Bibliographic record
Abstract
OBJECTIVES: Pharmacologic treatment of type 2 diabetes mellitus (T2DM) follows a stepwise approach. Typically, metformin monotherapy is first-line treatment, followed by other noninsulin antihyperglycemic agents (NIAHAs) or progression to insulin if glycated hemoglobin (A1C) targets are not achieved. We aimed to describe real-world patterns of basal insulin initiation in people with T2DM and A1C not at target despite treatment with at least 2 NIAHAs. METHODS: A retrospective cohort study was conducted using administrative health data from Alberta, Canada, among adults with T2DM, indexed on the first test with 7.0% < A1C < 9.5% (April 1, 2011, to March 31, 2019), with at least 2 previous NIAHAs but no insulin. Kaplan-Meier (KM) methodology was used to analyze time to basal insulin initiation, with stratification by index A1C. Annual patient status was categorized into 5 groups: basal insulin initiation, death, NIAHA intensification, no change in therapy (subgroups of A1C <7.1% and A1C ≥7.1% [clinical inertia]), or discontinuance. RESULTS: The cohort included 14,083 individuals. The KM cumulative probability of initiating basal insulin was 7.7% (95% confidence interval [CI] 7.3% to 8.2%) at 1 year, increasing to 43.1% (95% CI 42.1% to 44.1%) at 8 years of follow-up. Higher A1C levels were associated with greater proportions of basal insulin initiation. By year 8, proportions with NIAHA intensification and clinical inertia were 12.1% and 19.3%, respectively, relative to year 7. CONCLUSIONS: Despite current clinical practice guidelines recommending achieving A1C targets within 6 months, less than half of the individuals with T2DM and clear indications for basal insulin initiated treatment within 8 years. Efforts to reduce delays in basal insulin initiation are needed.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".