Accuracy of Two Ultrasound‐Guided Coracohumeral Ligament Injection Approaches: A Cadaveric Study
Bibliographic record
Abstract
BACKGROUND: Glenohumeral idiopathic adhesive capsulitis is a common shoulder condition that hinders functionality. Addressing the pathology has been extensively researched. Ultrasound (US)-guided injections have shown their efficacy. However, no study has been conducted to compare anatomical accuracy between different approaches in targeting the coracohumeral ligament (CHL). OBJECTIVE: To investigate whether US-guided injection of the CHL can be performed accurately using either the rotator interval (RI) or the coracoidal (CO) approach. METHODS: An experimental cadaveric case series. SETTING: Anatomy laboratory. SPECIMENS: Both shoulders of 13 Thiel-embalmed cadavers. INTERVENTIONS: Three physiatrists each injected a 0.1 mL bolus of colored dye in both shoulders of each cadaver using either the RI or the CO approach under US guidance. Each cadaver received a total of six injections (three injections per shoulder). The accuracy of the injection was determined following shoulder dissection by an anatomist. MAIN OUTCOME MEASURE: The accuracy of the US-guided injection of the CHL. RESULTS: The RI approach yielded 36 accurate injections, giving it an accuracy of 100%. With the CO approach two injections were deemed inaccurate yielding an accuracy of 94%. There was no significant difference in accuracy between all operators. CONCLUSIONS: US-guided injection of the CHL can be performed accurately with both the RI and CO approaches. The RI approach was likely to be more accurate.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".