Does acetabular coverage influence the clinical outcome of arthroscopically treated cam-type femoroacetabular impingement (FAI)?
Bibliographic record
Abstract
Aims: What represents clinically significant acetabular undercoverage in patients with symptomatic cam-type femoroacetabular impingement (FAI) remains controversial. The aim of this study was to examine the influence of the degree of acetabular coverage on the functional outcome of patients treated arthroscopically for cam-type FAI. Patients and Methods: (18.9 to 34.9). We used the Hip2Norm, an object-oriented-platform program, to perform 3D analysis of hip joint morphology using 2D anteroposterior pelvic radiographs. The lateral centre-edge angle, anterior coverage, posterior coverage, total femoral coverage, and alpha angle were measured for each hip. The presence or absence of crossover sign, posterior wall sign, and the value of acetabular retroversion index were identified automatically by Hip2Norm. Patient-reported outcome scores were collected preoperatively and at final follow-up with the Hip Disability and Osteoarthritis Outcome Score (HOOS). Results: At a mean follow-up of 2.7 years (1 to 8, sd 1.6), all functional outcome scores significantly improved overall. Radiographically, only preoperative anterior coverage had a negative correlation with the improvement of the HOOS symptom subscale (r = -0.28, p = 0.005). No significant difference in relative change in HOOS subscale scores was found according to the presence or absence of radiographic signs of retroversion. Discussion: Our study demonstrated the anterior coverage as an important modifier influencing the functional outcome of arthroscopically treated cam-type FAI. Cite this article: Bone Joint J 2018;100-B:831-8.
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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 | 0.003 |
| 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".