Bony Apprehension Test for Identifying Bone Loss in Patients With Traumatic Anterior Shoulder Instability: A Validation Study
Bibliographic record
Abstract
BACKGROUND: The presence of bone loss has important implications for the surgical treatment of patients with recurrent shoulder instability. The bony apprehension test (BAT) is a physical examination maneuver that was designed to improve specificity from the anterior apprehension test (AAT) in detecting critical bone loss. PURPOSE: The purpose of this study was to compare the BAT with the AAT and relocation test based on their abilities to predict critical bone loss. Several well-described criteria were utilized to capture critical (≥25%) and subcritical (≥13.5%) glenoid defects, as well as Hill-Sachs defects (≥19%). The ability of the BAT to predict bipolar bone loss was also assessed, as indicated by engaging Hill-Sachs defects and off-track lesions. STUDY DESIGN: Cohort study (diagnosis); Level of evidence, 1. METHODS: The study cohort included patients ≥18 years of age who were scheduled to undergo arthroscopic stabilization for traumatic anterior shoulder instability. Notable exclusion criteria included multidirectional shoulder instability, connective tissue disorders, and workers' compensation or litigation cases. Patients underwent physical examination immediately before surgery by the treating surgeon (ie, before the induction of anesthesia). Critical glenoid and humeral bone defects were measured on preoperative computed tomography scans. Hill-Sachs engagement and on- or off-track determination of bone loss were assessed arthroscopically and via computed tomography, respectively. RESULTS: A total of 52 patients were included in the study. In cases of subcritical glenoid bone loss (≥13.5%) and critical Hill-Sachs defects (≥19%), the BAT had good and fair specificity (82% and 72%, respectively) but poor sensitivity (40% and 39%). The BAT also had poor sensitivity (0%), specificity (67%), and positive predictive value (0%) for higher percentages of glenoid bone loss (≥25%). When engaging Hill-Sachs lesions were assessed, the BAT had excellent specificity (94%) and positive predictive value (94%) but poor sensitivity (43%) and negative predictive value (44%). Furthermore, the BAT performed poorly at predicting off-track humeral lesions. The AAT demonstrated 100% sensitivity and 0% specificity in detecting all measures of bone loss. CONCLUSION: The BAT performed poorly at identifying subcritical and critical bone loss and was not found to have any clinical value. Future work is needed to identify a physical examination test that could complement advanced imaging for preoperative assessment of critical bone loss.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".