Choosing drugs for the treatment of diabetic neuropathy
Bibliographic record
Abstract
INTRODUCTION: Diabetic sensorimotor polyneuropathy (DSP) affects 50% of diabetes patients and is painful in about 26%. Although disease-modifying therapies are not available for DSP, symptomatic treatments for painful diabetic neuropathy (PDN) are effective. AREAS COVERED: We performed a MEDLINE search on PubMed using the search terms: treatment diabetic neuropathy and treatment PDN. This review outlines the problem posed by DSP, the clinical presentation and the characterization of PDN. A discussion of disease-modifying interventions, including the benefits of strict glycemic control, is followed by a focus on interventions for PDN including antidepressants, anticonvulsants and other treatments. EXPERT OPINION: Disease modification in DSP remains an unmet need in clinical medicine affecting a large percentage of the population with concomitant healthcare costs. Strict glycemic control and attention to potential risk factors such as hypertension, hyperlipidemia and obesity may minimize DSP. Many patients benefit from treatment of their painful symptoms with anticonvulsants or antidepressants, but all are associated with significant side effects that limit their usefulness. There is a need for treatments of PDN with fewer side effects and more effective pain relief.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".