The Influence of Bone Loss on Glenoid Version Measurement: A Computer‐Modeled Cadaveric Analysis
Bibliographic record
Abstract
PURPOSE: To characterize how increasing computed tomography (CT)-quantified glenoid bone loss influences measured version. METHODS: Six embalmed cadaveric shoulders were used for this study. Glenoid bone defects were computer modeled in cadaveric shoulders; CT images were obtained and segmented using OsiriX software, creating 3-dimensional en face glenoids. Glenoid defects were made on CT images of intact glenoids superimposed with a glenoid clock face viewed en face to simulate anterior and posterior bone loss. Bony defects in various positions comprising 3%, 9.5%, and 19.5% were created posteriorly. Best-fit circles were superimposed to represent 10% and 25% defects anteriorly. Version was measured using the Friedman method. RESULTS: The average glenoid version measured 4° of retroversion, 2° after 10% anterior bone loss, and neutral version in the 25% bone loss group. Version was significantly altered when we compared intact glenoids versus 10% and 25% anterior glenoid bone loss (P < .001). Increasing from 10% to 25% bone loss showed a significant difference in measured version (P = .025). Posterior defects from the 6:30 to 8:30 clock-face position averaged 4.6° of retroversion; from the 6:30 to 9:30 clock-face position, 6.2° of retroversion; and from the 6:30 to 10:30 clock-face position, 8.7° of retroversion. When comparing glenoid defects at the 6:30 to 8:30 clock-face position with those involving the 6:30 to 9:30 and 6:30 to 10:30 clock-face positions (P < .001), a 1° correction may be used for every 5% of bone loss to account for version changes seen with bone loss. CONCLUSIONS: In this cadaveric analysis, glenoid version was altered in the setting of increasing posterior and anterior bone loss. A correction factor may be considered to account for this. When comparing glenoid defects at the 6:30 to 8:30 clock-face position with those involving the 6:30 to 9:30 and 6:30 to 10:30 clock-face positions (P < .001), a 1° correction may be used for every 5% of bone loss to account for version changes seen with bone loss. CLINICAL RELEVANCE: This cadaveric study shows that glenoid bone loss alters glenoid version, as measured by CT, in a meaningful way. This information is important in managing anterior and posterior shoulder instability, and correction of measured version should be considered in this setting to provide an accurate and comprehensive evaluation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".