Measurement of combined glenoid and Hill–Sachs lesions in anterior shoulder instability
Bibliographic record
Abstract
Background Recurrent glenohumeral anterior instability (RGAI) frequently induces combined glenoid and Hill–Sachs bone lesions and is a risk factor for soft tissue repair failure. This cohort study describes a simple preoperative quantification method for bone loss, the Clock method, the first that combines glenoid and humeral lesions. Methods Computed tomography scans of 34 shoulders with RGAI were twice reviewed by three independent observers, who measured bone lesions using the new Clock method and existing validated methods. Intra- and inter-observer reliability of the Clock method was evaluated (intraclass correlation coefficient). Pearson correlation was used to correlate Clock method with existing methods, and with function (Western Ontario Shoulder Instability, Quick-Disabilities of the Arm, Shoulder and Hand). Results Thirty-two patients met the inclusion criteria: three females and 29 males, mean age 28 years. The intra- and inter-observer reliability was excellent, with intraclass correlation coefficient ranging from 0.817 to 0.938 for the novel Clock method. Humeral Clock and Glenoid Clock strongly correlated with Humeral Ratio ( r = 0.882, p < 0.001) and Glenoid Surface Area and Glenoid Ratio ( r = 0.793 and 0.717, p < 0.001), respectively. The classic threshold of 25% of the glenoid diameter with the Glenoid Ratio method corresponds to 4 hours with the Glenoid Clock method. Conclusions The Clock method is quick and reliable, with more studies being needed to investigate whether it is correlated with surgical outcomes.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".