Can ultrasound-guided medial branch blocks be used to select patients for cervical facet joint radiofrequency neurotomy? A matched retrospective cohort validation study
Bibliographic record
Abstract
BACKGROUND: Medial branch blocks are used to select patients for cervical facet joint radiofrequency neurotomy (CRFN). Blocks are typically performed under fluoroscopic guidance (ie, fluoroscopy-guided blocks [FLBs]). The validity of ultrasound-guided blocks (USBs) is not well established. No prior research has compared cervical USB validity and FLB validity with CRFN outcome used as the criterion standard. OBJECTIVE: To evaluate cervical USB versus FLB validity with CRFN outcome used as the criterion standard. METHODS: Demographic and outcome data were extracted from the electronic medical records of 2 affiliated musculoskeletal pain management clinics for all patients between 2015 and 2023 inclusive who had cervical USB leading to CRFN. CRFN outcomes of each USB patient were compared with those of a matched FLB patient from the radiofrequency neurotomy (RFN) outcome database of the same clinics. Matching variables included patient age, sex, pain duration, diagnostic/prognostic block paradigm, and CRFN number. Each patient completed a numeric rating scale (NRS) pain score and Pain Disability Quality-of-Life Questionnaire (PDQQ) just before and 3 months after CRFN. At repeat CRFN, patients provided a retrospective estimate of the duration and average magnitude (percentage) of relief after the CRFN. RESULTS: USB and FLB groups were comprised of 27 patients (58 RFNs) and 38 patients (58 RFNs), respectively. Post-RFN NRS pain severity and PDQQ-Spine scores demonstrated comparable (P > .05) absolute improvements, proportion of patients achieving ≥50% improvement, and attainment of the minimum clinically important difference. Retrospective estimates of pain relief magnitude and duration were also comparable. CONCLUSIONS: This study finds cervical USB and FLB to be comparably valid as defined by their ability to predict CRFN outcome. Within the limitations of operator competence, USB can be used to select patients for CRFN.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".