A painful squat test provides limited diagnostic utility in CAM‐type femoroacetabular impingement
Bibliographic record
Abstract
PURPOSE: The purpose of this study is to determine the relationship between a symptomatic maximal squat and the presence of radiographic CAM-type femoroacetabular impingement (FAI) on magnetic resonance imaging (MRI) and to determine the sensitivity and specificity of a maximal squat test for the presence of radiographic CAM-type femoral deformity in an adult population. METHODS: In this pilot study, 76 consecutive patients were recruited from an outpatient clinic at McMaster University. All patients presented with pre-arthritic hip pain and were asked to perform a maximal squat. The results of this test were compared to magnetic resonance imaging and magnetic resonance angiographic (MRI and MRA) findings evaluating and characterizing CAM-type FAI deformity. RESULTS: The sensitivity and specificity of the maximal squat test were 75 % (56.6-88.5 %) and 41 % (27.0-56.8 %), respectively, for CAM-type FAI deformity. The positive and negative likelihood ratios were modest at 1.3 (0.9-1.7) and 0.6 (0.3-1.2), respectively. This means that a 30 % pre-test probability is improved to 36 % following a positive squat test and reduced to 20 % with a negative squat test. CONCLUSION: The maximal squat test was found to have marginal incremental diagnostic ability for CAM-type FAI. Its utility in the diagnostic evaluation of FAI remains limited. This survey elucidates areas of research for future studies relevant to the clinical diagnosis of FAI.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".