Open facet joint denervation as an adjunct in patients undergoing posterior lumbar decompression for spinal stenosis—a single blinded randomized controlled trial
Bibliographic record
Abstract
Background: Facet radiofrequency denervation is a prevalent procedure used to try and relieve back pain. Despite the increasing use of this treatment, its effectiveness has been questioned. In consideration of the conflicting reports in the literature, we sought to conduct a trial to study the short-term effect of facet denervation in patients undergoing lumbar laminectomy(s) to determine the short-term effect of adding facet denervation to patients undergoing lumbar laminectomy(s) where the anatomy was exposed, allowing an open technique to be used for the denervation. Methods: Sixty patients with a diagnosis of degenerative lumbar spinal stenosis who complained of neurogenic claudication and back pain for at least 3 months were randomized to undergo a lumbar laminectomy(s) either with or without facet joint denervation. Pain and self-reported function using a 10 cm visual analogue scale (VAS) and the Roland-Morris Disability Questionnaire (RMDQ) were measured before surgery and at patients’ 6-, 12- and 24-week follow-up clinic visit. Various parametric and non-parametric tests including the Chi-square, independent samples t -tests, the Mann Whitney U, Wilcoxon sign ranks, one-way ANOVA with a Bonferroni post hoc test were used to analyze the data. The RMDQ scores were analyzed between groups and within groups over time. Results: No differences in pain or functional ability were seen between groups. Both groups significantly improved in both pain and function from baseline to follow up times. Conclusions: Our findings do not support the addition of facet denervation for short-term treatment of back pain in patients undergoing lumbar laminectomy(s) for spinal stenosis within our study design.
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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) | 0.007 | 0.002 |
| 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 | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".