Contribution of the Bony Bankart in Calculating Glenoid Bone Loss
Bibliographic record
Abstract
Background: Determining the magnitude of glenoid bone loss in patients with anterior shoulder instability is an important step in guiding management. Most calculations to estimate the bone loss do not include the bony Bankart fragment. However, if it can be reduced and adequately fixed, the estimation of bone loss may be decreased. Purpose: To derive a simple equation to calculate the surface area of the bony fragment in Bankart fractures. Study Design: Case series; Level of evidence, 4. Methods: A total of 26 patients suspected of having clinically significant bone loss underwent computed tomography imaging preoperatively, and the percentage of glenoid bone loss (%BL) was approximated with imaging software using a freehand region of interest area measurement with and without the inclusion of the bony Bankart fragment. By assuming this bony fragment as a hemi-ellipse with height, H, and thickness, d, we represented the surface are of the bony piece ([Formula: see text]), and subtracted it from the overall %BL. They compared this value with the one found using imaging software. Results: Without the inclusion of the bony Bankart, the overall %BL by the standard true-fit circle measured using imaging software was 23.8% ± 9.7%. When including the bony Bankart, the glenoid %BL measured using imaging software was found to be 12.1% ± 8.5%. The %BL calculated by our equation with the bony Bankart included was 10% ± 11.1%. There was no statistically significant difference between the %BL values measured using the equation and the imaging software ( P = .46). Conclusion: Using a simple equation that approximates the bony Bankart fragment as a hemiellipse allowed for estimation of the glenoid bone loss, assuming that the fragment can be reduced and adequately fixed. This method may serve as a helpful tool in preoperative planning when there are considerations for incorporating the bony fragment in the repair.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".