Are paraspinous intramuscular injections of botulinum toxin a (BoNT-A) efficient in the treatment of chronic low-back pain? A randomised, double-blinded crossover trial
Bibliographic record
Abstract
BACKGROUND: Treatment for patients with chronic low-back pain (LBP) is a public health issue. Intramuscular injections of botulinum toxin A (BoNT-A) have shown an analgesic effect on LBP in two previous randomized controlled studies. The objective of the study was to verify the efficacy of paravertebral injections of BoNT-A in patients with LBP. METHODS: Patients were included in this phase 3 randomized double-blinded trial comparing the efficacy of BoNT-A versus placebo in a crossover study on LBP. Both groups received 200 units of BoNT-A in paravertebral muscles or a placebo, and vice versa at Day 120. The main judgment criterion was LBP intensity 1 month after the injections, evaluated by using a visual pain scale (VAS). Secondary assessment criteria included: LBP intensity 90 and 120 days after injection day; number of days when an allowed antalgic oral treatment was needed in between each evaluation; functional disability measured by the Quebec Back Pain Disability Scale; quality of life; inability to work; patient satisfaction in relation to the treatment's effect; spinal mobility; and strength of spinal muscles, measured by isokinetic technique. RESULTS: Nineteen patients completed the study. There was no significant difference between the groups' average LBP during the last 8 days at Day30 (p = 0.97). There was no significant difference between the two groups regarding the secondary assessment criteria (p > 0.05). CONCLUSIONS: Injections of BoNT-A in the paravertebral muscles were not found to be effective to relieve chronic LBP. The limits of the study are that the dose of BoNT-A used was lower than in other studies, and that the limited number of patients included may explain the negative results. TRIAL REGISTRATIONS: Identifiers: NCT03181802 . Unique Protocol ID: CHUBX2003.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".