Treating type 2 diabetes: moving towards precision medicine
Bibliographic record
Abstract
Type 2 diabetes is a prevalent condition with a rate of 6059 cases per 100 000 globally reported in 2017, and forecasted to increase to 7079 individuals per 100 000 by 2030.1 Several glucose-lowering medications have been developed to treat people with type 2 diabetes. Numerous guidelines2–5 for type 2 diabetes management recommend metformin as a first-line treatment, along with healthy lifestyle behaviours. Although guidelines provide recommendations on some second-line treatments for people with type 2 diabetes when metformin is contraindicated or insufficient to lower glycaemia, apart from comorbidities (eg, obesity, history of cardiovascular disease, and renal disease), there are other factors (such as renal insufficiency, medication costs, and patient preferences) that clinicians need to consider when selecting a second-line treatment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".