Barriers and new opportunities in developing effective therapies for diabetic neuropathy: International expert consensus recommendations
Bibliographic record
Abstract
BACKGROUND: Diabetic neuropathy (DN) affects up to half of individuals with type 1 and type 2 diabetes. Despite evidence that improving metabolic and cardiovascular health can slow its progression, DN remains a significant clinical challenge due to the lack of disease-modifying therapies and effective pain management strategies. This consensus aimed to identify gaps and recommend strategies to address these challenges. METHOD: A workshop, initiated by Steno Diabetes Centre Copenhagen and the Danish Diabetes and Endocrinology Academy, conducted a gap analysis based on insights from clinical studies, observational cohorts, and clinical practice. Online invitations targeted experienced clinicians, researchers, and drug developers committed to improving DN treatment through innovative clinical trials. Thirty-five participants from six countries reached consensus via a Delphi process on key steps to advance DN therapy. RESULT: Four critical barriers and needs were addressed: (1) Translating bench research to clinical practice, (2) Enhancing clinical trial design, (3) Improving outcome measures, and (4) Identifying effective treatments for painful DN. CONCLUSION: Successful interventional trials require robust outcome measures to capture clinically meaningful changes in DN phenotypes, providing the basis for developing effective, disease-modifying treatments.
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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 | 0.225 | 0.256 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.011 | 0.014 |
| Research integrity | 0.016 | 0.019 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".