Treatment of Chronic Low Back Pain With Successive Injections of Botulinum Toxin A Over 6 Months
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to evaluate the effects of two successive neurotoxin treatments for chronic low back pain using multiple pain rating scales in an open-label, prospective study. METHODS: Adult patients with chronic low back pain received multiple paraspinal muscle injections with a maximum dosing of 500 units of botulinum A toxin per session. Those with a beneficial clinical response received a second treatment at 4 months. Pain was assessed by visual analog scale (VAS), modified low back pain questionnaire (OLBPQ), and a clinical low back pain questionnaire (CLBPQ) at baseline, 3 weeks, 2 months, 4 months, and 6 months after the first treatment. RESULTS: Eighteen women and 42 men, ages 21 to 79 years (mean 46.6 years), with low back pain of a mean duration of 9.1 years were included. Significant improvement in back and radicular pain occurred at 3 weeks in 60% and at 2 months in 58% of the cohort. Beneficial clinical response to the first injection predicted response to reinjection in 94%. A significant minority of patients had a sustained beneficial effect from the first injection at 4 (16.6%) and 6 months (8.3%). Two patients had a transient flu-like reaction after the initial treatment. CONCLUSIONS: Botulinum toxin A improves refractory chronic low back pain with a low incidence of side effects. The beneficial clinical response is sustained with a second treatment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".