Management of Hyperglycemia in Type 2 Diabetes: A Consensus Algorithm for the Initiation and Adjustment of Therapy
Bibliographic record
Abstract
The consensus algorithm for the management of type 2 diabetes was developed on behalf of the American Diabetes Association and the European Association for the Study of Diabetes approximately 1 year ago (1,2). This evidence-based algorithm was developed to help guide health care providers to choose the most appropriate treatment regimens from an ever-expanding list of approved medications. The authors continue to endorse the major features of the algorithm, including the need to achieve and maintain glycemia within or as close to the nondiabetic range as is safely possible, the initiation of lifestyle interventions and treatment with metformin at the time of diagnosis, the rapid addition of medications and transition to new regimens when target glycemia is not achieved, and the early addition of insulin therapy in patients who do not meet target A1C levels. The availability of newly approved medications and the accrual of new clinical trial and other data should inform the algorithm. In this update, we primarily address one important issue that has received much recent attention: our current understanding of the advantages and disadvantages of the thiazolidinediones. In addition, we have revised the original Table 1 to include the dipeptidylpeptidase-4 inhibitor sitagliptin, which was not approved by the U.S. Food and Drug Administration at the time of our original publication (Table 1). We are mindful of the importance of not changing this consensus guideline in …
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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 | 0.026 | 0.030 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.011 | 0.005 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".