Perioperative Pregabalin Improves Pain and Functional Outcomes 3 Months After Lumbar Discectomy
Bibliographic record
Abstract
BACKGROUND: Patient outcome after lumbar discectomy for radicular low back pain is variable and the benefit is inconsistent. Many patients continue to experience pain 3 months after surgery. Pregabalin, a membrane stabilizer, may decrease perioperative central sensitization and subsequent persistent pain. METHODS: Forty patients undergoing lumbar discectomy were randomly allocated to receive either pregabalin (300 mg at 90 minutes preoperatively and 150 mg at 12 and 24 hours postoperatively) or placebo at corresponding times in a double-blinded manner. Our primary outcome was the change in the present pain intensity (PPI) (visual analog scale [VAS], 0-100 mm [PPI-VAS, McGill Pain Questionnaire]) from preoperatively to 3 months postoperatively. RESULTS: The decrease in PPI-VAS score at 3 months was greater in patients who received pregabalin (37.6 +/- 19.6 mm) (mean +/- sd) than those who received placebo (25.3 +/- 21.9 mm) (P = 0.08). The Roland Morris disability score at 3 months was less in patients who received pregabalin (2.7 +/- 2.4) than in those who received placebo (5.6 +/- 4.8) (P = 0.032). Pregabalin administration was associated with greater pain tolerance thresholds in both lower limbs compared with placebo at 24 hours postoperatively. CONCLUSION: Perioperative pregabalin administration is associated with less pain intensity and improved functional outcomes 3 months after lumbar discectomy.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".