Global challenges in diabetes research and care: which way forward? An appraisal from the EASD Global Council
Bibliographic record
Abstract
This review article, developed by the EASD Global Council, addresses the growing global challenges in diabetes research and care, highlighting the rising prevalence of diabetes, the increasing complexity of its management and the need for a coordinated international response. With regard to research, disparities in funding and infrastructure between high-income countries and low- and middle-income countries (LMICs) are discussed. The under-representation of LMIC populations in clinical trials, challenges in conducting large-scale research projects, and the ethical and legal complexities of artificial intelligence integration are also considered as specific issues. The development of global research networks and strategies for improved training, standardisation of data and enhanced accessibility to big data analytics to drive innovation and personalised medicine are recommended. With regard to diabetes care, inequalities in access to essential medications, particularly insulin and novel therapies, and disparities in healthcare infrastructure are discussed. Proposed initiatives include international support programmes, improved healthcare provider training and the inclusion of newer diabetes medications in essential drug lists. The importance of global screening programmes, a universal diabetes education curriculum and standardised healthcare checklists is also emphasised. Regarding healthcare organisation, the development of national diabetes registers, benchmarking performance across regions and strengthening international collaborations are highly advised. The role of diabetes specialists as care coordinators and the need for structured assessments to improve early intervention and long-term outcomes are also discussed. Ultimately, the EASD Global Council urges action for a unified, global approach to diabetes research and care to bridge the gap between scientific innovation and clinical practice, ensuring equitable healthcare worldwide.
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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 | 0.062 | 0.079 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.013 | 0.018 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".