Trends in basal insulin prescribing in older adults with chronic kidney disease in Ontario, Canada: A population‐based analysis from 2010 to 2020
Bibliographic record
Abstract
Abstract Aim To examine trends in basal insulin prescribing in older adults with chronic kidney disease (CKD). Materials and Methods We conducted a population‐based study of adults aged 66 years or older with treated diabetes from 1 January 2010 to 30 September 2020 in Ontario, Canada. We examined prevalent and incident prescriptions for human NPH, Levemir, glargine‐100, Basaglar, glargine‐300, and degludec insulin over 43 study intervals. We present trends in those with CKD, and in a subgroup, by estimated glomerular filtration rate (eGFR). To provide context for prescribing, we provide demographics, co‐morbidities, and the healthcare utilization of included patients. Results In CKD, use of basal insulin was about 2‐fold higher than in the general treated diabetes cohort. Prescriptions for NPH declined over time, while prescriptions for Levemir and glargine‐100 increased until 2018 then decreased. Following drug formulary approval (September 2018), prescriptions for glargine‐300 and degludec increased substantially. Incident prescriptions for basal insulin in CKD declined over time; however, in those with an eGFR of less than 30 ml/min/1.73m 2 , rates remained stable. In recent years, rates of degludec and glargine‐300 have rivalled glargine‐100. Conclusions In an era of new oral and injectable diabetes medications, the use of basal insulin has declined in older adults with CKD. However, in those with more advanced CKD, basal insulin, particularly newer analogues, remain a mainstay treatment.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| 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) | 0.001 | 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".