Trends in the prescription of anti‐diabetic medications in the United Kingdom: a population‐based analysis
Bibliographic record
Abstract
PURPOSE: Over the last decade, guidelines for the treatment of type 2 diabetes have increasingly favored tighter glycemic control, necessitating the use of more aggressive pharmacological therapy. The objective of this study was to describe trends in the prescription of anti-diabetic medications among patients with type 2 diabetes in the United Kingdom (UK). METHODS: Using the General Practice Research Database, we constructed a cohort of patients with type 2 diabetes. Diabetes was defined as the presence of a diagnosis of diabetes, HbA1c > or = 7%, or > or = 2 prescriptions for anti-diabetic medications. Analyses were conducted for the full cohort as well as a sub-cohort with incident diabetes. RESULTS: Our full cohort involved 67 981 patients and a total of 320 089 patient-years, and our sub-cohort involved 30 234 patients with incident diabetes and 111 890 patient-years. From 2000 to 2006, there was a substantial increase in the prescription rate of anti-diabetic medications. Overall, there were 9.6 prescriptions/patient-year in 2000, and this had increased to 14.8 prescriptions/patient-year in 2006. The greatest relative increase occurred in the prescription of thiazolidinediones. The greatest absolute increase occurred in the prescription of metformin, which surpassed sulfonylureas as the most commonly prescribed anti-diabetic medication among patients with type 2 diabetes in 2002. Among those with incident diabetes, overall prescription rates were 4.6 prescriptions/patient-year in 2000 and 13.6 prescriptions/patient-year in 2006. CONCLUSIONS: There was a substantial increase between 2000 and 2006 in the UK in the prescription of anti-diabetic medications. This increasingly aggressive pharmacological management is consistent with recent practice guidelines.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".