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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".