What is the optimal block selection paradigm for predicting a successful treatment outcome following sacral lateral branch radiofrequency neurotomy? A real-world cohort study
Bibliographic record
Abstract
Background: Outcomes following sacral lateral branch radiofrequency neurotomy (SLBRFN) likely depend on patient selection criteria; however, commonly used criteria vary considerably. Refinement of selection criteria for SLBRFN may improve treatment outcomes. This study investigated common prognostic block-based selection criteria and treatment success following SLBRFN. Methods: In this retrospective cohort study, consecutive patients from two Canadian musculoskeletal pain management clinics who underwent SLBRFN over a 6-year period (2016-2022) were identified by electronic medical record. Patients were categorized according to several prognostic block paradigms based on number of blocks (single vs. dual), block type (lateral branch block [LBB] vs. intra-articular block [IAB]), and subsequent percentage of pain relief. Six block criteria were established: 1 = LBB/LBB≥80 %; 2 = IAB/LBB≥80 %; 3 = LBB/LBB 50-79 %; 4 = IAB/LBB 50-79 %; 5 = LBB≥80 %; 6 = LBB 50-79 %. Treatment success was assessed at three months post-SLBRFN using two criteria: (1) the primary study outcome of ≥50 % numerical rating scale (NRS) pain reduction and (2) a secondary outcome of Pain Disability Quality-of-Life Questionnaire (PDQQ) score improvement by the minimal clinically important difference (MCID). Logistic regression analyses evaluated the association between block criteria and treatment success following SLBRFN. Results: ) were included. Cohort success rates for pain and functional improvement were 43.4 % (95 % CI: 37.8-49.3) and 46.6 % (95 % CI: 40.9-52.5), respectively. After adjusting for demographics and cannula type/SLBRFN technique, none of the odds ratios for the six prognostic block paradigms showed statistical significance. Conclusion: Nearly 50 % of patients who underwent SLBRFN reported clinically significant improvement in pain and disability at three months post-procedure, regardless of prognostic block selection criteria. These results suggest that multiple block strategies may determine eligibility for SLBRFN.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".