Can the FEAR Index Be Used to Predict Microinstability in Patients Undergoing Hip Arthroscopic Surgery?
Bibliographic record
Abstract
Background: Atraumatic hip instability, or microinstability, is a challenging diagnosis for clinicians to make. Several radiographic parameters have been proposed to help identify patients with instability as a means to direct treatment. The Femoro-epiphyseal Acetabular Roof (FEAR) index was recently offered as a parameter to predict instability in a borderline dysplastic population. Purpose: To evaluate the FEAR index in a series of predominantly nondysplastic patients undergoing hip arthroscopic surgery to determine if it can accurately predict patients with diagnosed microinstability at the time of surgery. Study Design: Cohort study (diagnosis); Level of evidence, 2. Methods: A consecutive series of 200 patients undergoing hip arthroscopic surgery were evaluated for microinstability intraoperatively. Microinstability was diagnosed based on previously published criteria. Retrospectively, radiographic parameters were measured including the lateral center edge angle of Wiberg (LCEA), Tönnis angle, physeal scar angle, and FEAR index. Patients were excluded if they previously had any type of bony procedures performed, underwent prior open hip surgery or total hip arthroplasty of the ipsilateral hip, had osteoarthritis (Tönnis grade >1), or had any radiographic features of moderate-to-severe acetabular dysplasia including an LCEA <18°. Results: After applying exclusion criteria, 167 hips in 150 patients were analyzed. Based on an intraoperative assessment, 96 hips (57.5%) were considered stable, and 71 hips (42.5%) had signs of microinstability (unstable group). Patients in the unstable group had fewer radiographic findings of femoroacetabular impingement and higher rates of borderline dysplasia. All 4 measured angles were found to have excellent interobserver agreement. The FEAR index was significantly more positive in the unstable group compared with the stable group (−7.8° vs −11.3°, respectively; P = .004). A more positive FEAR index was also found in patients meeting intraoperative criteria for instability, with the exception of chondral wear pattern. Unstable nondysplastic patients (LCEA ≥25°, Tönnis angle ≤10°) also were found to have higher FEAR index values (−9.0° vs −12.0°, respectively; P = .012). A FEAR index cut-off of −5.0° was associated with a specificity of 92.4% and accuracy of 69.4% for predicting instability in a nondysplastic population. Conclusion: The FEAR index was validated to improve the recognition of unstable patients preoperatively across a population with both borderline dysplastic and nondysplastic features.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".