Assessment of bone loss in anterior shoulder instability
Bibliographic record
Abstract
Anterior shoulder dislocations commonly result in predictable patterns of osseous injury on both the glenoid and humeral side. The presence of bone loss contributes to the risk of recurrent dislocations, as well as the success of surgical intervention. For example, in patients with glenoid lesions comprising >25% of the glenoid surface or Hill-Sachs lesions that “engage” the glenoid rim, the recurrence rate has been reported to be as high as 67% after soft tissue Bankart repair. The range of injury severity and anatomic variations in soft tissue and bony injury patterns associated with anterior shoulder instability makes identification and quantification of these lesions critical prior to surgical intervention. Historically, bony lesions on the glenoid and humeral side were considered independently. More recently, the interaction between the two and their summative effects on recurrence and operative outcomes has become better understood. The purpose of this review is to provide an overview of the historical methods of identifying bony lesions, as well as an update on current concepts in quantifying bone loss in anterior shoulder instability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| 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".