PainVision® Apparatus for Assessment of Efficacy of Pulsed Radiofrequency Combined with Pharmacological Therapy in the Treatment of Postherpetic Neuralgia and Correlations with Measurements
Bibliographic record
Abstract
Objective. PainVision device was a developed application for the evaluation of pain intensity. The objective was to assess the efficacy and safety of pulsed radiofrequency (PRF) combined with pharmacological therapy in the treatment of postherpetic neuralgia (PHN). We also discussed the correlation of the measurements.Method. Forty patients with PHN were randomized for treatment with PRF combined with pharmacological therapy (PRF group, n=20 ) or pharmacological therapy (control group, n=20 ) at postoperative 48 hours. The efficacy measure was pain degree (PD) that was assessed by PainVision and visual analog scale (VAS), short form Mcgill pain questionnaire (SF-Mcgill), and numeric rate scale sleep interference score (NRSSIS). Correlations between PD, VAS, SF-Mcgill, and NRSSIS were determined.Results. The PD for persistent pain (PP) and breakthrough pain (BTP) at postoperative 48 hours assessed by PainVision were significantly lower in PRF group than in control group (PD-PP, P<0.01 ; PD-BTP, P<0.01 ). PD and VAS were highly correlated for both persistent pain ( r=0.453 , ρ=0.008 ) and breakthrough pain ( r=0.64 , ρ=0.001 ).Conclusion. PRF was well tolerated and superior to isolated pharmacological therapy in the treatment of PHN. PainVision device showed great value in the evaluation of pain intensity and PD had an excellent correlation with VAS and SF-Mcgill.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.005 | 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".