The Efficacy of Adding Electromagnetic Therapy or Laser Therapy to Medications in Patients With Diabetic Peripheral Neuropathy
Bibliographic record
Abstract
Introduction: Diabetes mellitus (DM) is a common disease with a highly significant burden among the Saudi population. This study aimed to investigate the effects of adding either magnetic or laser therapy to medications in patients with diabetic peripheral neuropathy (DPN). Methods: Seventy-one medically controlled diabetic patients were randomly assigned to 1) Magnetic group: 26 patients were exposed to magnetic therapy for 20 minutes/session, 2 sessions/week, for 3 months 2) Laser group: 25 patients were exposed to laser therapy with intensity 5.7 J/cm2 for 30 minutes/session, 2 times/week, for 3 months. 3) Drug group: 20 patients received only the regular medications for diabetic control and pain analgesia. Pain and neuropathy were assessed by the visual analog scale (VAS) and the Toronto Clinical Neuropath Scoring System (TRCNSS). Conduction velocities and amplitudes of peroneal and sural nerves were measured by electromyography. Results: The results showed significant increases in conduction velocities and amplitudes in both magnetic and laser groups in parallel with significant reductions in TRCNSS. Non-significant changes were obtained only after using only medications (P>0.05). The mean values of VAS reduced significantly in the three groups. The least significant differences showed significant changes among the three groups, whereas non-significant differences were obtained between both magnetic and laser groups. Conclusion: There were non-significant differences between both magnetic and laser therapy groups. Addition of either magnetic or laser therapy to medications could bring extra positive benefits to patients with DPN. Both magnetic and laser therapy can be applied with medications for the treatment of patients with DPN.
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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) | 0.001 | 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) | 0.002 | 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".