The Characteristics of the Favard E4 Glenoid Morphology in Cuff Tear Arthropathy: A CT Study
Bibliographic record
Abstract
Background: Cuff tear arthropathy (CTA) is characterized by superior migration of the humeral head with superior erosion of the glenoid. Rarely, humeral head migration can be anteroinferior with associated anterior erosion of the glenoid, a pattern described by Favard as the type E4 glenoid. The purpose of this retrospective imaging study was to analyze the 2D and 3D characteristics of the E4 glenoid. Methods: A shoulder arthroplasty database of 258 cuff tear arthropathies was examined to identify patients with an E4 type deformity. This resulted in a study cohort of 15 females and 2 males with an average age of 75 years. All patients had radiographs and CT scans available for analysis. CT-scan DICOM (Digital Imaging and Communications in Medicine) data were uploaded to a validated three-dimensional (3D) imaging software. Muscle fatty infiltration, glenoid measurements (anteversion, inclination), and humeral head subluxation according to the scapular plane were determined. Results: The mean anteversion and inclination of the E4 cohort were 32° ± 14° and −5° ± 2, respectively. The mean anterior subluxation was 19% ± 16%. All cases had severe grade 3 or 4 fatty infiltration of the infraspinatus, whereas only 65% had grade 3 or 4 subscapularis fatty infiltration. A significant correlation existed between glenoid anteversion and humeral head subluxation (p < 0.001), but no correlation was found with muscle fatty infiltration. The CT analysis demonstrated an acquired erosive biconcave morphology in 11 patients (65%) and monoconcavity in 6 patients (35%). Conclusion: The E4 type glenoid deformity in cuff tear arthropathy is characterized by an anterior erosion and anteversion associated with anterior subluxation of the humeral head.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".