How Useful Is the Flexion–Adduction–Internal Rotation Test for Diagnosing Femoroacetabular Impingement: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: Clinicians use the flexion, adduction, and internal rotation (FADIR) test in the diagnosis of femoroacetabular impingement (FAI). However, the diagnostic utility of this test remains unclear. The purpose of this review was to determine the utility of the FADIR test in diagnosing FAI. DATA SOURCES: MEDLINE, EMBASE, and PubMed were searched using relevant key terms and study screening was performed in duplicate. Patient demographics, diagnostic imaging, and summary measures (eg sensitivity, specificity, etc.) of the FADIR test in patients with FAI were recorded. MAIN RESULTS: Eight studies of levels III (87.5%) and IV (12.5%) evidence were included. Four hundred fifty-two patients (622 hips) with a mean age of 27.0 ± 9.0 were examined. Alpha (75.1%) and/or center-edge (26.8%) angles were used to diagnose hips with FAI. X-ray (78.9%), magnetic resonance imaging (MRI) (16.2%), and computed tomography (CT) (4.8%) were used to confirm the diagnosis of FAI. The sensitivity when confirmed by x-ray, MRI, or CT was 0.08 to 1, 0.33 to 1 and 0.90, respectively. The specificity when confirmed by x-ray and MRI was 0.11 and 1, respectively. CONCLUSIONS: Although the overall utility of the FADIR test in diagnosing FAI remains unclear given its moderate sensitivity and specificity, it may be a useful screening tool for FAI because of its low risk. Clinicians should consider the variability in sensitivity and specificity values reported and the low quality of literature available. Future studies should use large sample sizes and consistent radiographic measurements to better understand the usefulness of this physical examination maneuver in diagnosing FAI. LEVEL OF EVIDENCE: Level IV, Systematic Review of Level III and IV studies.
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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 | 0.009 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.008 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".