Perineural Platelet-Rich Plasma for Diabetic Neuropathic Pain, Could It Make a Difference?
Bibliographic record
Abstract
OBJECTIVE: To evaluate the clinical effect of perineural platelet-rich plasma (PRP) injection for pain and numbness alleviation in diabetic peripheral neuropathy (DPN). STUDY DESIGN: A randomized prospective clinical trial. SETTING: Pain clinic and Rheumatology and Rehabilitation Departments, Assiut University Hospital. METHODS: Sixty adult patients with type II DM accompanied by DPN of at least six months' duration were assessed by modified Toronto Clinical Neuropathy Score (mTCNS) and randomly allocated into two groups. Group I underwent ultrasound-guided perineural PRP injection and medical treatment, and Group II received medical treatment only. Patients were followed up at months 1, 3, and 6 with regard to pain and numbness visual analog scale (VAS) and mTCNS scores. RESULTS: Significant improvement was recorded in pain and numbness VAS scale scores in group I vs group II (P ≤ 0.001 during the whole study period for both parameters); at the same time, mTCNS improved in group I in comparison with group II with P = 0.01, 0.001, and <0.001 at months 1, 3, and 6, respectively. CONCLUSIONS: Perineural PRP injection is an effective therapy for alleviation of diabetic neuropathy pain and numbness and enhancement of peripheral nerve function.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".