Validity of the Alpha Angle Measurement on Plain Radiographs in the Evaluation of Cam-type Femoroacetabular Impingement
Bibliographic record
Abstract
BACKGROUND: Cam-type femoroacetabular impingement is secondary to lack of concavity at the anterosuperior femoral head-neck junction, resulting in reduced femoral head-neck offset and femoral head asphericity. This morphologic deformity can be detected by MRI and plain radiographs and quantified using the alpha angle. QUESTIONS/PURPOSES: We evaluated the accuracy and reproducibility of plain radiography in the diagnosis of cam-type deformity. METHODS: Sixty-eight patients (37 females, 31 males) with a mean age of 38 years (range, 17-60 years) were treated for intraarticular hip pathology with 43 hips having cam-type femoroacetabular impingement and 25 having isolated labral tears. All patients had alpha angle measurements made on plain radiographs (AP pelvis, crosstable lateral, Dunn view) and multiplanar MRI using an alpha angle of more than 50.5° as the gold standard. RESULTS: The Dunn view had a sensitivity of 91%, specificity of 88%, positive predictive value of 93%, negative predictive value of 84%, and accuracy of 90% for diagnosing the cam deformity associated with femoroacetabular impingement. The Pearson correlation coefficients between the MRI and plain radiography values were 0.702, 0.552, and 0.349 for the Dunn, crosstable lateral, and AP views, respectively. CONCLUSIONS: Our observations validate the clinical use of the Dunn view in the evaluation of the femoral head-neck contour in cam-type femoroacetabular impingement. LEVEL OF EVIDENCE: Level I, diagnostic study. See Guidelines for Authors for a complete description of levels of evidence.
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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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".