Group Releases New Guideline on Options for Treating Painful Diabetic Neuropathy
Bibliographic record
Abstract
A NEW GUIDELINE FOR USING EFfective therapies for patients with painful diabetic neuropathy has been issued by the American Academy of Neurology. The guideline, released in April, was established through a systematic review of the literature from 1960 to August 2008 that reported on the efficacy of pharmacological treatments (such as anticonvulsants, antidepressants, and opioids) and nonpharmacological treatments (such as electrical stimulation, magnetic field treatment, low-intensity laser treatment, and Reiki massage). The guideline authors rated the therapies based on the quality of the evidence (Bril V et al. Neurology. 2011; 76[20]:1758-1765). The burning or tingling pain of diabetic neuropathy adversely affects patients’ quality of life. It is estimated that about 16% of patients with diabetes have painful diabetic neuropathy. And while treatments are available, an estimated 39% of cases remain untreated and about 1 in 8 patients do not even report the condition to their physicians. Vera Bril, MD, the lead guideline author and director of the neuromuscular section of the University of Toronto in Toronto, Ontario, Canada, explained why the academy decided to publish the guideline. “We did it because there are new treatments available and people are all over the place in thinking of what works and does not work,” Bril said. What works, at least according to the most rigorous study, is the anticonvulsant pregabalin, which the authors said is effective in lessening the pain of diabetic neuropathy and in improving quality of life; the drug also lessens sleep interference, although the effect size is small. Pregabalin was the only treatment to receive the more rigorous level A recommendation by the authors.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.014 | 0.012 |
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".