Anterior Center-Edge Angle Is Less Reliable Than Anterior Wall Index to Predict Anterior Coverage of the Femoral Head
Bibliographic record
Abstract
Background: No consensus is available regarding which radiographic measurement most accurately correlates with anterior coverage of the femoral head. Purpose: (1) To determine the correlation between 2 measurements of anterior wall coverage: total anterior coverage (TAC) calculated from radiographs and equatorial anterior acetabular sector angle (eAASA) calculated from computed tomography (CT) scans; (2) to define the correlation between anterior center-edge angle (ACEA) and anterior wall index (AWI) with TAC and eAASA; and (3) to investigate what other radiographic metrics may help predict anterior coverage. Study Design: Cohort study (Diagnosis); Level of evidence, 3. Methods: The authors retrospectively reviewed 77 hips (48 patients) for which radiographs and CT scans were obtained for reasons other than hip-related pain. Mean age of the population was 62 ± 22 years; 48 (62%) hips were from female patients. Two observers measured lateral center-edge angle (LCEA), AWI, Tönnis angle, ACEA, CT-based pelvic tilt, and CT-based acetabular version, with all Bland-Altman plots within 95% agreement. Correlation between intermethod measurements was estimated with a Pearson coefficient. Linear regression was used to test the ability of baseline radiographic measurements to predict both TAC and eAASA. Results: Pearson coefficients were r = 0.164 (ACEA vs TAC; P = .155), r = 0.170 (ACEA vs eAASA; P = .140), r = 0.58 (AWI vs TAC; P = .0001), and r = 0.693 (AWI vs eAASA; P < .0001). Multiple linear regression model 1 showed that AWI (β = 17.8; 95% CI, 5.7 to 29.9; P = .004), CT acetabular version (β = −0.45; 95% CI, −0.71 to −0.22; P = .001), and LCEA (β = 0.33; 95% CI, 0.19 to 0.47; P = .001) were useful to predict TAC. Multiple linear regression model 2 revealed that AWI (β = 25; 95% CI, 15.67 to 34.4; P = .001), CT acetabular version (β = −0.48; 95% CI, −0.67 to −0.29; P = .001), CT pelvic tilt (β = 0.26; 95% CI, 0.12 to 0.4; P = .001), and LCEA (β = 0.21; 95% CI, 0.1 to 0.3; P = .001) accurately predicted eAASA. Model-based estimates and 95% CIs using 2000 bootstrap samples from the original data were 6.16 to 28.6 for AWI in model 1 and 15.1 to 34.26 for AWI in model 2. Conclusion: There was a moderate to strong correlation between AWI and both TAC and eAASA, whereas ACEA correlated weakly with the former measurements, thus not being useful to quantify anterior acetabular coverage. Other variables such as LCEA, acetabular version, and pelvic tilt may also help predict anterior coverage in asymptomatic hips.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.002 | 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".