Effect of vitamin D on the peripheral neuropathy in type 2 diabetic patients
Bibliographic record
Abstract
Background: Current therapies for diabetic neuropathy have not been satisfactory despite the frequent attempts made and various medications introduced. Given the lower level of vitamin D in diabetic patients than in normal population, the present study evaluated the role of vitamin D in the treatment of diabetic neuropathy. Methods: A total of 90 type II diabetic patients with a history of neuropathy for at least one month based on Toronto clinical score system and low level of vitamin D, who had referred to Kermanshah Diabetes Research Center, Iran, were included in this clinical study. The patients underwent treatment with 50000 U vitamin D for three months, once per week. The level of vitamin D, HbA1C and the score of neuropathy were measured before and after three months treatment and the results were analyzed. Results: After treatment, vitamin D level significantly increased which was favorable in 85% of the patients. Also, neuropathy level decreased from 9.5±2.5 (before treatment) to 7.0±2.5 (after treatment), which was statistically significant. Moreover, the HbA1C level significantly decreased from 8.6±1.6 to 7.4±1.4. However, no significant correlation was reported between neuropathy level reduction, changes of vitamin D level and HBA1C level. Conclusion: The results of the current study indicated that vitamin D has no therapeutic effect on diabetic neuropathy and increase of pain threshold level may be non-specifically due to the placebo effect of vitamin D.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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 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".