Do cervical medial branch blocks have a therapeutic role? a prospective cohort study
Bibliographic record
Abstract
BACKGROUND: Cervical medial branch blocks (CMBB) are used for the diagnosis of facet joint-related pain. There have been several reports suggesting that they can provide a benefit that significantly outlasts the expected duration of diagnostic blocks. We undertook this study to determine the frequency and extent of this effect in clinical practice. METHODS: 179 patients undergoing 830 individual block levels and who had previously reported 50% relief following a CMBB were recruited using a prospective cohort design and blocks were performed in an outpatient setting. Outcomes monitored at baseline and every 2 weeks for 12 weeks included numerical pain scores, Neck Disability Index and Patient Global Impression of Change. In addition, the Pain Catastrophizing Scale, Generalized Anxiety Disorder 7 and Patient Health Questionnaire 9 were completed every 4 weeks. RESULTS: Statistically significant differences were found for all measures of pain, emotional and physical functioning, when comparing 2 and 12-week measurements to baseline. At 2 weeks, 62.2% of patients reported a 30% or greater decrease in pain scores, which decreased to 22.7% at 12 weeks. Exploratory analysis found no association between age, opioid use, pain etiology, previous surgery, levels treated (upper vs lower cervical) and change in Numerical Rating Score between weeks 0 and 2, as well as between weeks 2 and 12. CONCLUSION: Our findings suggest that a proportion of patients undergoing CMBB may experience clinical benefits that exceed the expected duration of local anesthetics. Further research is required to determine the potential clinical applications of these findings. TRIAL REGISTRATION NUMBER: NCT04852393.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".