Striving for early effective glycaemic and weight management in type 2 diabetes: A narrative review
Bibliographic record
Abstract
Despite the recognition by key guidelines that achieving early glycaemic control has important benefits in individuals with type 2 diabetes (T2D) and that addressing excess adiposity is one of the central components of comprehensive person-centred T2D care, a substantial proportion of individuals with T2D do not meet their metabolic treatment goals. Prior treatment paradigms were limited by important treatment-associated risks such as hypoglycaemia and body weight gain. Therefore, a more conservative, sequential approach to treatment was typically utilized. One potential consequence of this approach has been a missed opportunity to achieve a 'legacy effect', where early treatment to reach glycaemic targets is associated with enduring long-term benefits in T2D. Additionally, while previous treatment approaches have addressed core defects in T2D, including insulin resistance and β-cell function decline, they have been unable to address one of the underlying causal abnormalities-excess adiposity. Here, we review currently available evidence for the beneficial long-term effects of early glycaemic control and management of body weight in people with T2D and discuss potential next steps.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".