Accuracy of unguided and ultrasound guided Coracohumeral ligament infiltrations – a feasibility cadaveric case series
Bibliographic record
Abstract
BACKGROUND: Coracohumeral ligament (CHL) thickening, contracture, and fibroplasia have been identified in glenohumeral idiopathic adhesive capsulitis (GHIAC). The CHL is the main structure responsible for the range of motion limitations. Favorable outcomes have been reported with CHL surgical release. Intra-articular glenohumeral joint corticosteroid infiltrations are utilized to disrupt the inflammatory process and reduce pain in GHIAC. The aim of this study was to investigate whether the CHL could be accurately targeted with a periligamentous infiltration. METHODS: A convenience sample of 12 unembalmed cadaver shoulders (mean age: 74.5 years, range 66-87 years) without evidence of previous injury or surgery were utilized in this exploratory double factor feasibility cadaveric (unguided and ultrasound (US) guided) case series. Two clinicians trained in musculoskeletal infiltration techniques carried out the infiltrations on each shoulder with colored latex. One clinician infiltrated without guidance, the other with US-guidance. The injecting clinicians were blinded to the others infiltration procedure and the order was randomized. An anatomist blinded to the infiltration order performed a shoulder dissection and recorded the infiltrate location. Percentage calculation for accuracy of infiltration and a chi-square evaluation of the difference between unguided and US-guided infiltrations was applied. RESULTS: An accuracy of 75% was achieved for unguided infiltration and 80% for US-guided infiltration techniques. Chi-squared indicated there was no significant difference (p = 0.82) between the unguided and US-guided techniques. CONCLUSION: US-guided and unguided infiltrations achieved good accuracy targeting the CHL, suggesting infiltrations can specifically and accurately target the CHL. In vivo investigation using such infiltration techniques are warranted.
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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 | 0.004 | 0.006 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".