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Record W2961441734 · doi:10.1093/pm/pnz140

Perineural Platelet-Rich Plasma for Diabetic Neuropathic Pain, Could It Make a Difference?

2019· article· en· W2961441734 on OpenAlexaboutno aff
Manal Hassanien, Abdelraheem Elawamy, Emad Zarief Kamel, Walaa A. Khalifa, Ghada Mohamed Abolfadl, Al Shimaa Roushdy, Randa A. El Zohne, Yasmine S. Makarem

Bibliographic record

VenuePain Medicine · 2019
Typearticle
Languageen
FieldMedicine
TopicPeriodontal Regeneration and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineVisual analogue scalePeripheral neuropathyPlatelet-rich plasmaNeuropathic painAnesthesiaRehabilitationRandomized controlled trialClinical trialPeripheralSurgeryDiabetes mellitusPhysical therapyInternal medicinePlatelet

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.259
Threshold uncertainty score0.800

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.026
GPT teacher head0.281
Teacher spread0.255 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations48
Published2019
Admission routes1
Has abstractyes

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