Pregabalin in the Treatment of Refractory Neuropathic Pain: Results of a 15-Month Open-Label Trial
Bibliographic record
Abstract
OBJECTIVE: Neuropathic pain associated with postherpetic neuralgia (PHN) and painful diabetic peripheral neuropathy (DPN) can be intractable and may not respond to commonly used treatments, such as tricyclic antidepressants (TCAs) and opioids. This long-term, open-label study was a preliminary evaluation of pregabalin for patients whose pain had been judged refractory to other treatments for neuropathic pain. DESIGN: Patients had previously participated in double-blind, placebo-controlled, randomized trials of pregabalin in DPN and PHN. They had moderate to severe neuropathic pain despite treatment with gabapentin, a TCA, and a third medication (e.g., other anticonvulsants, opioid, selective serotonin reuptake inhibitor, tramadol). Flexible-dosage pregabalin 150-600 mg/day was taken for 3-month periods followed by 3- to 28-day pregabalin "drug holidays," with an analysis up to 15 months (five treatment cycles). Pain intensity was measured using the visual analog scale of the Short-Form McGill Pain Questionnaire. RESULTS: In total, 81 patients were included in this analysis. Pregabalin 150-600 mg/day was associated with clinically meaningful and sustained pain reduction during each treatment cycle. During pregabalin "drug holidays," pain quickly returned to baseline levels; it was reduced again when pregabalin was reinstated. CONCLUSIONS: These results suggest that pregabalin may be beneficial in patients with neuropathic pain who have had an unsatisfactory response to treatment with other medications.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".