Postherpetic neuralgia: a descriptive analysis of patients seen in pain clinics*1
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Postherpetic neuralgia (PHN) is a common ailment that pain specialists must often cope with. The goal of the present survey is to investigate the pain characteristics of 119 PHN patients with persistent pain seen at different pain clinics. Methods information on demographic features, pain characteristics (MPQ-SV, VAS, VRS), and treatment was recorded by means of a standard case report form. RESULTS: Gender, age, location, and treatment were consistent with previous reports. Antidepressant, antiepileptic, and analgesic drugs were the most commonly used by patients. The patients seen in pain clinics are probably a subset of severe cases of PHN, where delayed referral to these units plays an important role. The most frequent qualitative features of the disease were the McGill Pain Questionnaire-Spanish Version (MPQ-SV). These features may change in relation to illness evolution; electric shocklike pain and emotional distress were significantly higher after 6 months of evolution and age (those younger than 70 were more able to locate painful areas and to feel pain as pricking or sharp), and gender (men selected spatial pressure and traction pressure more frequently). None of the explored variables was relevant to predicting pain intensity. CONCLUSIONS: This study reveals a subset of patients, mostly suffering from long-term PHN, where pain persists. The most frequent qualitative traits of PHN patients are described. Some variables were involved in modulation of pain characteristics in these patients. The effect of study design in interpretation of results is discussed.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".