Analgesic effect of clobazam in chronic low‐back pain but not in experimentally induced pain
Bibliographic record
Abstract
BACKGROUND: Chronic pain is frequently associated with hypersensitivity of the nervous system, and drugs that increase central inhibition are therefore a potentially effective treatment. Benzodiazepines are potent modulators of GABAergic neurotransmission and are known to exert antihyperalgesic effects in rodents, but translation into patients are lacking. This study investigates the effect of the benzodiazepine clobazam in chronic low-back pain in humans. The aim of this study is to explore the effect of GABA modulation on chronic low-back pain and on quantitative sensory tests. METHODS: In this double-blind cross-over study, 49 patients with chronic low-back pain received a single oral dose of clobazam 20 mg or active placebo tolterodine 1 mg. Pain intensity on the 0-10 numeric rating scale and quantitative sensory tests were assessed during 2 h after drug intake. RESULTS: Pain intensity in the supine position was significantly reduced by clobazam compared to active placebo (60 min: 2.9 vs. 3.5, p = 0.008; 90 min: 2.7 vs. 3.3, p = 0.024; 120 min: 2.4 vs. 3.1, p = 0.005). Pain intensity in the sitting position was not significantly different between groups. No effects on quantitative sensory tests were observed. CONCLUSIONS: This study suggests that clobazam has an analgesic effect in patients with chronic low-back pain. Muscle relaxation or sedation may have contributed to the effect. Development of substances devoid of these side effects would offer the potential to further investigate the antihyperalgesic action of GABAergic compounds. SIGNIFICANCE: Modulation of GABAergic pain-inhibitory pathways may be a potential future therapeutic target.
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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) | 0.000 | 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) | 0.003 | 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".