Diagnostic block and radiofrequency ablation of the acromial branches of the lateral pectoral and suprascapular nerves for shoulder pain: a 3D cadaveric study
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Acromial branches of the lateral pectoral and suprascapular nerves have been proposed as targets for diagnostic block and radiofrequency ablation to treat superior shoulder pain; however, the nerve capture rates of these procedures have not been investigated. The objectives of this study were to use dissection and 3D modeling technology to determine the course of these acromial branches, relative to anatomical landmarks, and to evaluate nerve capture rates using ultrasound-guided dye injection and lesion simulation. METHODS: Ultrasound-guided dye injections, targeting the superior surface of coracoid process and floor of supraspinous fossa, were performed (n=5). Furthermore, needles targeting the superior and posterior surfaces of the coracoid process were placed under ultrasound guidance to simulate needle electrode position (n=5). Specimens were dissected, digitized, and modeled to determine capture rates of acromial branches of lateral pectoral and suprascapular nerves. RESULTS: The course of acromial branches of lateral pectoral and suprascapular nerves were documented. Dye spread capture rates: acromial branches of lateral pectoral and suprascapular nerves were captured in all specimens. Lesion simulation capture rates: (1) when targeting superior surface of coracoid process, the entire acromial branch of lateral pectoral nerve was captured in all specimens and (2) when targeting posterior surface of coracoid process, the acromioclavicular and bursal branches of acromial branch of suprascapular nerve were captured in all specimens; coracoclavicular branch was captured in 3/5 specimens. CONCLUSIONS: This study supports the anatomical feasibility of ultrasound-guided targeting of the acromial branches of lateral pectoral and suprascapular nerves. 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.005 | 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".