Extended-release gabapentin for failed back surgery syndrome: results from a randomized double-blind cross-over study
Bibliographic record
Abstract
Persistent pain after lumbar surgery (failed back surgery syndrome [FBSS]) remains a leading indication for chronic analgesia. However, no analgesics have proven efficacious for this condition. Although trials have evaluated gabapentinoids for chronic low back pain, none of these trials focused solely on FBSS. This randomized, double-blind cross-over trial evaluated the efficacy of gabapentin (1800 mg/day) for FBSS. Eligible patients had a diagnosis of FBBS, an average daily pain score of at least 4 of 10, a neuropathic pain component (indicated by the PainDetect), and reported at least half of their pain radiating in their lower extremity. Participants were randomized to 2, 7-week study periods separated by a 10-day washout. The primary outcome measure was a 0 to 10 numeric rating scale (NRS) of average pain. Secondary measures included the McGill Pain Questionnaire and Patient Global Impression of Change. The treatment effect was analyzed using a mixed effect analysis of covariance with fixed effects for treatment, period, and baseline 7-day mean NRS pain score and a random effect for the participant. The outcome of the model was the mean 7-day NRS score for the last 7 days of each treatment period. Thirty-two participants were randomized and included in the primary analysis; 25 completed both study periods. No difference was detected between treatments on any outcome measure, including the primary (least square mean difference in NRS: -0.01 confidence interval: [-0.22 to 0.20]). Given the escalating rate of complex lumbar surgery, future research to develop novel therapies for this prevalent syndrome is needed.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 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".