A dietary intervention for chronic diabetic neuropathy pain: a randomized controlled pilot study
Bibliographic record
Abstract
BACKGROUND: Diabetic neuropathy is a common and often debilitating condition for which available treatments are limited. Because a low-fat plant-based diet has been shown to improve glycemic control in individuals with type 2 diabetes, we hypothesized that such a diet would reduce painful symptoms of diabetic neuropathy. METHODS: In this 20-week pilot study, individuals with type 2 diabetes and painful diabetic neuropathy were randomly assigned to two groups. The intervention group was asked to follow a low-fat, plant-based diet, with weekly classes for support in following the prescribed diet, and to take a vitamin B12 supplement. The control group was asked to take the same vitamin B12 supplement, but received no other intervention. At baseline, midpoint and 20 weeks, clinical, laboratory and questionnaire data were collected. Questionnaires included an analog 'worst pain' scale, Michigan Neuropathy Screening Instrument, global impression scale, Short Form McGill Pain Questionnaire, Neuropathy Total Symptom Score, a weekly pain diary and Norfolk Quality of Life Questionnaire. RESULTS: After 20 weeks, body weight change with the intervention was -6.4 kg (95% confidence interval (CI) -9.4 to -3.4, P<0.001) in an effect size analysis. Electrochemical skin conductance in the foot improved by an average of 12.4 microseimens (95% CI 1.2-23.6, P=0.03) with the intervention in an effect size analysis. The between-group difference in change in pain, as measured by the McGill pain questionnaire, was -8.2 points (95% CI -16.1 to -0.3, P=0.04). Michigan Neuropathy Screening Instrument questionnaire score change was -1.6 points (95% CI -3.0 to -0.2, P=0.03). CONCLUSIONS: Improvements were seen in some clinical and pain measures. This pilot study suggests the potential value of a plant-based diet intervention, including weekly support classes, for treating painful diabetic neuropathy.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".