The effectiveness of sacral lateral branch radiofrequency neurotomy for posterior sacroiliac joint complex pain in patients selected by dual sacral lateral branch blocks; A real-world cohort study
Bibliographic record
Abstract
Background: Previous study of spinal neurotomy procedures indicates that stringent block selection improves outcomes. However, this pattern is not established for sacral lateral branch radiofrequency neurotomy (SLBRFN). Few SLBRFN studies have used stringent block selection criteria such as ≥80 % pain reduction following dual sacral lateral branch blocks (SLBB). Objective: Evaluate the effectiveness of SLBRFN in patients with ≥80 % pain relief following dual SLBBs. Methods: Retrospective single-arm cohort study of consecutive patients from two Canadian musculoskeletal and pain clinics who underwent first-time SLBRFN after report of ≥80 % pain relief following dual diagnostic SLBBs. Patients were identified by electronic medical record query between 2016 and 2022. The primary outcome was the proportion of individuals with a ≥50 % reduction in the numeric pain rating scale (NPRS) score three months after SLBRFN. Secondary outcomes included the proportion of responders achieving the minimal clinically important difference (MCID) on the pain disability quality-of-life questionnaire (PDQQ), and the duration and mean percentage of pain relief among those with recurrent symptoms after a successful SLBRFN. Results: Of the 70 participants included, 32 (45.7 %; 95 % CI = 34.6-57.3) reported a ≥50 % reduction in NPRS, and 35 (50.0 %; 95 % CI = 38.6-61.4) achieved the MCID on the PDQQ at 3-months. Among the 17 patients who reported a return of symptoms, the mean duration of relief was 8.0 ± 3.5 months, and the mean percentage of pain relief was 77.9 % ± 16.4 %. Logistic regression models revealed that the use of multi-tined RF probes and lower patient BMI were associated with treatment success. Discussion/conclusion: SLBRFN reduced pain and disability in approximately 50 % of patients at 3 months when selected using relatively restrictive selection criteria. Treatment success was associated with multi-tined RF probe type and lower patient BMI. Larger prospective studies assessing long-term outcomes are needed to further evaluate the impact of different selection criteria and techniques on SLBRFN effectiveness.
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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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".