Evaluation of suprascapular nerve radiofrequency ablation protocols: 3D cadaveric needle placement study
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Image-guided intervention of the suprascapular nerve is a reported treatment to manage chronic shoulder joint pain. The suprascapular nerve is conventionally targeted at the suprascapular notch; however, targeting of its branches, the medial and lateral trunks, which are given off just posterior to the notch has not been considered. Since the lateral trunk supplies the posterior supraspinatus and articular branches to the glenohumeral joint capsule, while the medial trunk provides motor innervation to the anterior region, it may be possible to preserve some supraspinatus activation if the medial trunk is spared. The main objective was to investigate whether midpoint between suprascapular and spinoglenoid notches is the optimal target to capture articular branches of lateral trunk while sparing medial trunk. METHODS: In 10 specimens, using ultrasound guidance, one 17 G needle was placed at the suprascapular notch and a second at midpoint between suprascapular and spinoglenoid notches. The trunks and needles were exposed in the supraspinous fossa, digitized and modeled in 3D. Lesion volumes were added to the models to asses medial and lateral trunk capture rates. Mean distance of needle tips to origin of medial trunk was compared. RESULTS: Conventional notch technique captured both lateral and medial trunks, whereas a midpoint technique captured only lateral trunk. Mean distance of needles from the origin of medial trunk was 5.10±1.41 mm (notch technique) and 14.99±5.53 mm (midpoint technique). CONCLUSIONS: The findings suggest that the midpoint technique could spare medial trunk of suprascapular nerve, while capturing lateral trunk and articular branches. Further clinical investigation is required.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".