Pregabalin monotherapy for relief of pain associated with fibromyalgia: Durability of pain results of a 14-week, double-blind, placebo-controlled trial
Bibliographic record
Abstract
Neuropathic pain (NeP) is a chronic condition that occurs frequently with diabetes and herpes zoster infection. In addition to potentially lasting many years, the relationship between chronic pain, anxiety/depression, and sleep, also referred to as the triad of pain, causes functional impairment in many areas of life.The aim of this study was to examine the 12-week cost-effectiveness of 2 treatments of NeP, pregabalin versus gabapentin, in managing diabetic peripheral neuropathy (DPN) and postherpetic neuralgia (PHN) in a Canadian setting.A stochastic simulation model evaluating NeP treatment was adapted to the Canadian setting. Using data from clinical trials of pregabalin (150–600 mg/d) and gabapentin (900–3600 mg/d), the model simulated 12-week treatment outcomes for patients with DPN or PHN. Resource utilization was identified through an Internet-based survey among 80 Canadian physicians. Utility values (as measured using the EuroQol EQ-5D) were obtained from 126 NeP patients participating in a cross-sectional study conducted at Canadian primary care sites. The economic analysis was expressed as incremental cost per quality-adjusted life year (QALY) gained and as incremental cost per day with no or mild pain. Model sensitivity to changes in key parameters was assessed.Following 12-week treatment, compared with gabapentin, pregabalin was projected to result in 6 and 9 additional days with no or mild pain for patients with DPN and PHN, respectively. Pregabalin therapy was estimated to provide an additional 0.0047 QALY and 0.0086 QALY over gabapentin administration, for DPN and PHN, respectively. Mean (SE) direct costs per DPN patient were estimated as Can $837.53 ($37.31) (2004 dollars) with gabapentin and $818.49 ($36.50) with pregabalin, and per PHN patient as $720.61 ($33.70) with gabapentin and $667.07 ($25.33) with pregabalin. Model findings were sensitive to variation in the dose and corresponding cost of the comparator, but not in other parameters.Based on the results of this analysis, in the treatment of NeP associated with DPN or PHN, pregabalin was a dominant or cost-effective treatment strategy compared with gabapentin.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| 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) | 0.005 | 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".