Effect of gastroretentive gabapentin (Gralise) on postmastectomy pain syndrome: a proof-of-principle open-label study
Bibliographic record
Abstract
INTRODUCTION: Chronic pain is a common and debilitating complication following breast surgery. One of the most challenging for treatment is the neuropathic pain condition, postmastectomy pain syndrome (PMPS). Gabapentin is a pharmacotherapy for neuropathic pain disorders; however, its once-daily, gastroretentive formulation, Gralise, has not been evaluated in PMPS. OBJECTIVE: To evaluate the safety and effectiveness of Gralise in patients with moderate-to-severe PMPS. METHODS: The primary effectiveness endpoint was a change in the worst pain intensity score from baseline to completion of 8 weeks of Gralise therapy. The secondary endpoints included the change in mood, coping behavior, sleep, and function. Sensitivity to experimental stimuli was tested before and after treatment via quantitative sensory testing. The incidence and type of adverse event were used to evaluate the safety and tolerability of Gralise. RESULTS: Twenty-one patients with confirmed moderate-to-severe PMPS were enrolled. Nineteen of 21 (90.5%) patients completed the 8-week treatment with Gralise. A significant positive change was found in pain intensity, pain impact, and sleep. There was no change in sensory testing scores. Of total, 63.16% of patients reported reduction in present pain, 78.95% in average pain, 89.47% in worst pain, and 84.21% in overall pain severity at posttreatment visit. No significant adverse effects were noted in the study. LIMITATIONS: Variation in type of breast surgery, small sample size, lack of placebo control. CONCLUSION: There was a significant improvement in pain and sleep, and Gralise was well tolerated in patients with PMPS. Further investigation is warranted.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| 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 | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".