Evaluation of Perifacet Injections and Paraspinal muscle rehabilitation in Treatment of Low Back Pain. A Randomised Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Lumbar paraspinal muscle dysfunction and Low Back Pain are strongly correlated. The best treatment for non-specific Low Back Pain is still controversial. OBJECTIVE: To evaluate the efficacy of lumbar multifidus muscle retraining exercises and perifacet multifidus injections in the treatment of Low Back Pain. MATERIAL AND METHODS: 63 patients with non-specific LBP, with or without leg pain, and magnetic resonance images of paraspinal muscle degeneration only, were randomised to one of three treatment groups: A- Back education and standard physiotherapy for 10 weeks, B- Back education and gym ball exercise for 10 weeks or C- Perifacet injection into the lumbar multifidus muscle with methylprednisolone. The Oswestry Disability Index was used as the primary outcome measure and the SF-36, modified Zung depression index, modified somatic perception and McGill pain questionnaires were used as secondary outcome measures. RESULTS: 56 patients completed the trial. The Oswestry Disability Index improved in general from a mean of 29.9 to 25.9, but there were no statistically significant differences between the groups. Low back pain improved most in group C (P<0.02), while physical activities and social functioning were improved the most in group B (P<0.03). CONCLUSION: Perifacet injection and back education including a gym ball exercise program may be more effective than back education alone in relieving pain and improving physical capacity respectively. Back education including gym ball exercise could be used for non-specific Low Back Pain, as the ultimate goal should be to restore function.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".