Radial Collateral Ligament of the Elbow
Bibliographic record
Abstract
OBJECTIVES: An abnormality of the radial collateral ligament (RCL) in the setting of lateral epicondylitis can indicate a poor clinical outcome; therefore, accurate assessment is important. The purpose of this study was to characterize the proximal RCL attachment, or footprint, as seen on sonography using cadaveric dissection correlation and magnetic resonance arthrography. METHODS: For the first part of this study, 4 cadaveric elbow specimens were imaged with sonography before and after dissection to characterize the RCL. After Institutional Review Board approval with consent waived, 26 consecutive magnetic resonance (MR) arthrograms of the elbow were identified. The sonograms and MR arthrograms were retrospectively reviewed to measure the length of the RCL footprint and its percentage of the combined RCL and common extensor tendon (CET) humeral footprints. RESULTS: The mean RCL footprint length and percentage of the combined RCL and CET footprints were 8.4 mm (range, 7.4-10.0 mm) and 54% as measured from the elbow specimen sonograms and 9.1 mm (range, 6.4-12.5 mm) and 54% as measured from the MR arthrograms. The mean RCL footprint length combining data from specimens and MR arthrograms was 8.9 mm (range, 6.4-12.5 mm), covering 54% of the combined RCL and CET footprints. CONCLUSIONS: The RCL can be differentiated from the CET on sonography with knowledge of the RCL humeral footprint extent, which measured 8.9 mm in length and comprised 54% of the combined RCL and CET footprints.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".