Validating the Glenoid Track Concept Using Dynamic Arthroscopic Assessment
Bibliographic record
Abstract
Background: Failure after isolated Bankart repair has led surgeons to consider when to address the Hill-Sachs lesion, which is thought to be a contributor to recurrent instability. One approach utilizes the glenoid track concept to determine whether a Hill-Sachs lesion is classified as “off-track,” suggesting that the addition of a remplissage procedure may aid stability. However, the accuracy and reliability of using this approach require validation using an appropriate reference. Purpose: To determine the accuracy and reliability of using the glenoid track concept against dynamic arthroscopic assessment of Hill-Sachs lesion engagement. Study Design: Cohort study (diagnosis); Level of evidence, 3. Methods: A total of 49 patients undergoing arthroscopic Bankart repair surgery for recurrent traumatic anterior shoulder instability were enrolled in this diagnostic validation study. Shoulders were classified as on-track or off-track using 3-dimensional computed tomography (3DCT) and static arthroscopic measurements. These classifications were compared with dynamic arthroscopic assessment (engagement of the Hill-Sachs lesion on the anterior glenoid rim in the ‘athletic position’) to determine their accuracy and reliability. Results: The 3DCT-based measurements to determine glenoid track status had a higher positive predictive value (66% vs 42%), higher specificity (47% vs 42%), and higher accuracy (65% vs 59%) compared with static arthroscopic measurements. Static arthroscopic measurements to determine glenoid track status had a higher negative predictive value (96% vs 64%) and higher sensitivity (96% vs 81%) compared with 3DCT-based measurements. Interrater reliability (Krippendorff α) was ‘fair’ for determining the glenoid track status using 3DCT (0.368; 95% CI, 0.217-0.519) and ‘moderate’ for static arthroscopic measurements (0.523; 95% CI, 0.364-0.666). Intrarater reliability (intraclass correlation coefficient [ICC] 3, k) was ‘moderate’ for 3DCT measurements (0.660; 95% CI, 0.444-0.798) and ‘good’ for static arthroscopic measurements (0.769; 95% CI, 0.629-0.862). Conclusion: Determining glenoid track status using either 3DCT or static arthroscopic measurements yielded moderate accuracy and reliability. Surgeons using the glenoid track concept to aid surgical decision-making in traumatic recurrent anterior shoulder instability should utilize 3DCT or static arthroscopic measurements with caution.
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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 | 0.021 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 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".