Indication for Computed Tomography Scan in Shoulder Instability: Sensitivity and Specificity of Standard Radiographs to Predict Bone Defects After Traumatic Anterior Glenohumeral Instability
Bibliographic record
Abstract
BACKGROUND: Quantifying glenohumeral bone loss is key in preoperative surgical planning for a successful Bankart repair. HYPOTHESIS: Simple radiographs can accurately measure bone defects in cases of recurrent shoulder instability. STUDY DESIGN: Cohort study (diagnosis); Level of evidence, 2. METHODS: A true anteroposterior (AP) view, alone and in combination with an axillary view, was used to evaluate the diagnostic properties of radiographs compared with computed tomography (CT) scan, the current gold standard, to predict significant bone defects in 70 patients. Sensitivity, specificity, and positive and negative predictive values were evaluated and compared. RESULTS: Detection of glenoid bone loss on plain film radiographs, with and without axillary view, had a sensitivity of 86% for both views and a specificity of 73% and 64% with and without the axillary view, respectively. For detection of humeral bone loss, the sensitivity was 8% and 17% and the specificity was 98% and 91% with and without the axillary view, respectively. Regular radiographs would have missed 1 instance of significant bone loss on the glenoid side and 20 on the humeral side. Interobserver reliabilities were moderate for glenoid detection (κ = 0.473-0.503) and poor for the humeral side (κ = 0.278-0.336). CONCLUSION: Regular radiographs showed suboptimal sensitivity, specificity, and reliability. Therefore, CT scan should be considered in the treatment algorithm for accurate quantification of bone loss to prevent high rates of recurrent instability.
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".