Imaging methods for quantifying glenoid and Hill-Sachs bone loss in traumatic instability of the shoulder: a scoping review
Bibliographic record
Abstract
BACKGROUND: Glenohumeral instability is a common problem following traumatic anterior shoulder dislocation. Two major risk factors of recurrent instability are glenoid and Hill-Sachs bone loss. Higher failure rates of arthroscopic Bankart repairs are associated with larger degrees of bone loss; therefore it is important to accurately and reliably quantify glenohumeral bone loss pre-operatively. This may be done with radiography, CT, or MRI; however no gold standard modality or method has been determined. A scoping review of the literature was performed to identify imaging methods for quantifying glenohumeral bone loss. METHODS: The scoping review was systematic in approach using a comprehensive search strategy and standardized study selection and evaluation. MEDLINE, EMBASE, Scopus, and Web of Science were searched. Initial selection included articles from January 2000 until July 2013, and was based on the review of titles and abstracts. Articles were carried forward if either reviewer thought that the study was appropriate. Final study selection was based on full text review based on pre-specified criteria. Consensus was reached for final article inclusion through discussion amongst the investigators. One reviewer extracted data while a second reviewer independently assessed data extraction for discrepancies. RESULTS: Forty-one studies evaluating glenoid and/or Hill-Sachs bone loss were included: 32 studies evaluated glenoid bone loss while 11 studies evaluated humeral head bone loss. Radiography was useful as a screening tool but not to quantify glenoid bone loss. CT was most accurate but necessitates radiation exposure. The Pico Method and Glenoid Index method were the most accurate and reliable methods for quantifying glenoid bone loss, particularly when using three-dimensional CT (3DCT). Radiography and CT have been used to quantify Hill-Sachs bone loss, but have not been studied as extensively as glenoid bone loss. CONCLUSIONS: Radiography can be used for screening patients for significant glenoid bone loss. CT imaging, using the Glenoid Index or Pico Method, has good evidence for accurate quantification of glenoid bone loss. There is limited evidence to guide imaging of Hill-Sachs bone loss. As a consensus has not been reached, further study will help to clarify the best imaging modality and method for quantifying glenohumeral 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 | 0.024 | 0.097 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.045 | 0.033 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".