From glucose lowering agents to disease/diabetes modifying drugs: a “SIMPLE” approach for the treatment of type 2 diabetes
Bibliographic record
Abstract
During the last decade we experienced a surge in the number of glucose lowering agents that can be used to treat patients with type 2 diabetes. Especially important are the discoveries that sodium glucose co-transporter type 2 inhibitors (SGLT2i) and glucagon-like peptide-1 receptor agonists (GLP-1 RA) improve patients' cardiovascular and renal outcomes. Accordingly, various medical associations have updated their guidelines for the treatment of diabetes in this new era. Though not agreeing on every issue, these position-statements generally share a detailed and often complex workflow that may be too complicated for the busy and overworked primary care setting, where the majority of patients with type 2 diabetes are managed in many countries. Other guidelines, generally those from the cardiology associations focus primarily on the population of patients with high risk for or pre-existing cardiovascular disease, which represent only the minority of patients with type 2 diabetes. We believe that we should re-define SGLT2i and GLP-1 RA as diabetes/disease modifying drugs (DMDs) given the recent evidence of their cardiovascular and renal benefits. Based on this definition we have designed a SIMPLE approach in order to assist primary care teams in selecting the most appropriate therapy for their patients. We believe that most subjects newly diagnosed with type 2 diabetes should initiate early combination therapy with metformin and a prognosis changing DMD. The decision whether to use GLP-1 RA or SGLT2i should be made based on specific patient's risk factors and preferences. Importantly, DMDs are known to have a generally safe side-effect profile, with lower risk for hypoglycemia and weight gain, further promoting their wider usage. Early combination therapy with DMDs may improve the multiple pathophysiological abnormalities responsible for type 2 diabetes and its complications, thus resulting in the greatest long term benefits.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.018 |
| Insufficient payload (model declined to judge) | 0.009 | 0.011 |
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".