Diabetes prevention and treatment: a global perspective
Bibliographic record
Abstract
In recent years there has been an increasing focus on precision medicine, including in diabetology [ 1 , 2 , 3 ]. However, many studies in this field have two limitations. First, most studies on diabetes prevention and treatment originate from high-income countries (HICs), whereas the burden of diabetes is highest and increasing most rapidly in low- and middle-income countries (LMICs) [ 4 , 5 ]. Second, and closely related to the first limitation, most studies do not sufficiently capture the global diversity of diabetes aetiology, phenotypes and therapeutic needs based on ancestry, ethnicity and geography [ 6 ]. However, there is clear evidence that this diversity is clinically relevant [ 7 , 8 ]. Currently, global differences in diabetes epidemiology and pathophysiology as well as disparities in diabetes prevention and management are insufficiently understood [ 4 , 8 , 9 ].
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.012 | 0.019 |
| Insufficient payload (model declined to judge) | 0.016 | 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".