Dissection study of the innervation of the anterior elbow joint: laying the anatomical foundation for minimally invasive pain relief procedures to treat elbow joint pain
Bibliographic record
Abstract
BACKGROUND: Minimally invasive pain relief treatments targeting articular nerves have emerged as treatment options for chronic joint pain, bridging the gap between conservative management and surgical intervention. These procedures have not been developed for the elbow joint due to insufficient anatomical knowledge of the articular branches innervating the elbow joint. OBJECTIVE: To determine origin, course, frequency, landmarks, and capsular distribution of the articular branches supplying the anterior elbow joint. DESIGN: Anatomical dissection study. METHODS: Six upper extremity specimens were meticulously dissected. Articular branches were traced from their origin on the brachial plexus to their termination. Origin, course, termination, and landmarks of each branch were documented and photographed in high resolution. A frequency map was generated to visualize the capsular areas supplied by each articular branch. RESULTS: The musculocutaneous and/or median nerve gave rise to an extramuscular branch supplying the capsule deep to brachialis (83%). The median nerve supplied the medial capsule via a direct articular branch (17%), branches to flexor digitorum superficialis (100%), and a branch to pronator teres (67%). The anterior interosseus nerve supplied the capsule over the proximal radioulnar joint (67%). The radial nerve provided articular innervation to the lateral capsule via a radial branch to brachialis (83%), branches to extensor carpi radialis longus (100%), and branches to supinator (100%). CONCLUSIONS: Detailed anatomical knowledge of the articular branches supplying the anterior elbow joint is essential for identifying potential targets for therapeutic intervention and exploring selective blockade of articular nerves to alleviate pain originating from specific capsular areas.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".