Normal and Pathoanatomy of the Arthritic Shoulder: Considerations for Shoulder Arthroplasty
Bibliographic record
Abstract
The glenohumeral joint is a highly mobile, complex articulation that relies not only on the bony support between the humeral head and glenoid but also on appropriate balance and tension of the surrounding soft-tissue structures. Recreating the normal anatomic relationships is a basic premise in joint arthroplasty, which can be challenging in shoulder arthroplasty, as the normal glenohumeral anatomy has considerable variation from patient to patient. Also, as the anatomy of the glenohumeral joint becomes distorted with advanced shoulder pathology, it becomes a challenge to return the shoulder to its premorbid anatomic state. Failure to restore normal anatomic parameters after shoulder arthroplasty has been shown to have deleterious effects on postoperative function and implant survival. As the recognition of this has grown, shoulder prostheses have evolved to allow for considerable more variation in an attempt to recreate patient-specific anatomy. However, understanding the progression of shoulder pathology to better predict the patient's premorbid anatomy remains limited. A thorough understanding of the premorbid and pathologic anatomy of the glenohumeral joint will aid in preoperative planning and intraoperative execution and lead to a more predictable reconstruction of the shoulder, which is critical for a successful outcome after shoulder arthroplasty.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".