Radiographic sclerotic contour loss in the identification of glenoid bone loss
Bibliographic record
Abstract
PURPOSE: Quantification of glenoid bone loss guides surgical management in the setting of anterior shoulder instability. Glenoid defects resulting in ≥20 % articular area loss require bony reconstruction. The objective of this study was to evaluate the utility of sclerotic glenoid contour loss on true anteroposterior radiography in the detection of varying quantities of simulated glenoid bone loss using a cadaveric model. METHODS: Eight cadaveric scapulae with full radiographic sclerotic contour were osteotomized to produce glenoid surface area reductions of 10-50 %. Radiography was performed initially and following each osteotomy, and assessed by an orthopedic surgeon and radiologist twice. Quantity of glenoid loss was compared using Fisher's exact test. Sensitivity, specificity, and reliability analyses were performed. RESULTS: On the first radiographic review, sclerotic contour loss was detected in 6 out of 8 scapulae with 50 % area loss, but only 1 out of 8 scapulae with 20 % area loss. There was a significantly higher proportion of radiographs containing sclerotic contour loss for defects with 50 % area loss compared to those with 0-25 % loss (p ≤ 0.02). In the detection of ≥20 % area loss, sclerotic contour loss had a sensitivity of 33-43 % and specificity of 88-100 %. Moderate inter-observer reliability (Cohen's kappa value of 0.42-0.53) and intra-observer reliability (kappa value of 0.46-0.58) were found. CONCLUSION: Radiographic sclerotic contour loss is commonly observed in radiographs of scapulae with 40-50 % glenoid area loss and less often with smaller lesions. However, this finding lacks utility in discerning specific quantifications of glenoid bone loss. In a clinical setting, sclerotic contour loss suggests the presence of a large glenoid defect that may require bony reconstruction. However, an intact sclerotic contour does not rule out significant 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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".