Effect of posture and femoral neck osteochondroplasty on femur‐acetabulum clearance in patients with cam‐type femoroacetabular impingement
Bibliographic record
Abstract
It is not clear whether femoral neck osteochondroplasty achieves its objective of increasing femoroacetabular clearance. We used an upright open magnetic resonance imaging scanner to image the hip joint in multiple postures to explore the effect of posture and femoral neck osteochondroplasty on femur-acetabulum clearance in patients with cam-type femoroacetabular impingement. We recruited 13 consecutive patients scheduled to undergo arthroscopic femoral neck osteochondroplasty and completed assessments on 10 patients. We scanned each subject before surgery and at 6 months post-op in supine and 3 other physiological postures: supine 90° flexion with adduction and internal rotation (FADIR), sitting deep (maximal flexion with internal rotation and adduction), and sitting crossed leg (maximal adduction with flexion and internal rotation). We measured the alpha angle, which describes the severity of cam deformity, and the beta angle, which defines joint clearance. We also evaluated hip flexion, internal rotation, and adduction before and after surgery. Femoral neck osteochondroplasty significantly decreased alpha angle by 23.9° ± 4.6° (p = 0.001) and increased beta angle across all postures by 28.1° ± 6.3° (p = 0.002). An increase in beta angle represented a decreased chance of impingement. Femoral neck osteochondroplasty significantly increased flexion by an average of 8.6° in the sitting deep posture after surgery (p = 0.007) which might indicate an improvement of joint function. These findings lend support to the hypothesis that arthroscopic osteochondroplasty accomplishes its stated goals of increasing bone-bone clearance in the hip joint and improving joint mechanics for the static postures assessed.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".