Substitution of Gabapentin Therapy with Pregabalin Therapy in Neuropathic Pain due to Peripheral Neuropathy
Bibliographic record
Abstract
OBJECTIVE: To determine the utility of substitution of pregabalin (PGB) for gabapentin (GBP) therapy in the relief of neuropathic pain (NeP) in patients with peripheral neuropathy (PN). DESIGN: A cohort study was performed examining PGB substitution in patients who were GBP responders (> or =30% NeP relief on a visual analog scale [VAS]) or GBP nonresponders after prolonged GBP use, with further comparison to patients receiving continuous GBP therapy. SETTING: Patients with PN and related NeP requiring GBP therapy were evaluated in a tertiary care neurological clinic at 0, 6, and 12 months. OUTCOME MEASURES: Pain severity (Visual Analog Score [VAS]) was the primary outcome measure, while quality of life (European Quality of Life - 5 Domains [EQ-5D] and EQ-5D VAS) and occurrence of adverse events were secondary outcome measures. RESULTS: Both GBP responder and nonresponder groups had additional NeP relief of about 25% following substitution of PGB after 6 and 12 months, while improved EQ-5D VAS was identified in the GBP nonresponder group. There were no serious adverse events for either medication, while GBP nonresponders discontinued PGB in more than 30% of cases due to inefficacy or adverse events. CONCLUSIONS: Randomized, controlled, blinded head-to-head studies of GBP and PGB have not been published. The results of this open-label assessment of PGB substitution for GBP suggest that PGB may provide additional pain relief and possible improvement in quality of life above that received by GBP use in patients with NeP due to PN.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".