Variability in quantifying the Hill-Sachs lesion: A scoping review
Bibliographic record
Abstract
Background: Currently, is there no consensus on a widely accepted measurement technique for calculating the Hill-Sachs lesion (HSL). The purpose of this review is to provide an overview of the techniques and imaging modalities to assess the HSL pre-operatively. Methods: Four online databases (PubMed, Embase, MEDLINE, and COCHRANE) were searched for literature on the various modalities and measurement techniques used for quantifying HSLs, from data inception to 20 November 2021. The Methodological Index for Non-Randomized Studies tool was used to assess study quality. Results: Forty-five studies encompassing 3413 patients were included in this review. MRA and MRI showed the highest sensitivity, specificity, and accuracy values. Intrarater and interrater agreement was shown to be the highest amongst MRA. The most common reference tests for measuring the HSL were arthroscopy, radiography, arthro-CT, and surgical techniques. Conclusion: MRA and MRI are reliable imaging modalities with good test diagnostic properties for assessment of HSLs. There is a wide variety of measurement techniques and imaging modalities for HSL assessment, however a lack of comparative studies exists. Thus, it is not possible to comment on the superiority of one technique over another. Future studies comparing imaging modalities and measurement techniques are needed that incorporate a cost-benefit analysis.
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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 | 0.098 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.021 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".