Agreement Between Magnetic Resonance Imaging and Arthroscopic Evaluation of the Shoulder Joint in Primary Anterior Dislocation of the Shoulder
Bibliographic record
Abstract
OBJECTIVE: To determine the effectiveness of magnetic resonance imaging in identifying shoulder pathology in patients with primary traumatic dislocation of the shoulder and to compare these findings with findings at the time of arthroscopic surgery. DESIGN: Correlation between arthroscopy and magnetic resonance imaging. PATIENTS: Sixteen patients, aged 18 to 30 years, who were randomized to the surgical arm of a study comparing the effectiveness of immediate arthroscopic surgery with immobilization and rehabilitation for primary traumatic anterior dislocation of the shoulder, were included in this study. INTERVENTIONS: Each patient underwent magnetic resonance imaging and a videotaped "tour" of the shoulder prior to any surgical intervention. MAIN OUTCOME MEASURE: Magnetic resonance scans and videotapes were reviewed for the presence or absence of abnormalities in 8 features of the shoulder, and concordant and discordant findings were evaluated. RESULTS: There was moderate correlation for superior labral lesions (kappa = 0.60) and fair agreement for rotator cuff tear (kappa = 0.355). When the joint capsule was assessed, there was only fair agreement for both the presence of an abnormality (kappa = 0.310) and redundancy and tear (kappa = 0.394). Both methods were sensitive for the detection of Hill-Sachs lesions (kappa = 1.0), although there was only moderate agreement (kappa = 0.44) on estimation of size. There was perfect agreement for the detection of Bankart lesions or equivalent capsulolabral disruption (kappa = 1.0). CONCLUSIONS: Magnetic resonance imaging can be considered a valuable tool for the detection of Hill-Sachs and Bankart lesions associated with primary traumatic anterior dislocations of the shoulder. Its ability to detect other pathologic lesions, however, is limited.
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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 | 0.012 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".