Predicting Collapse in Osteonecrosis of the Femoral Head Using a New Method: Preserved Angles of Anterior and Lateral Femoral Head
Bibliographic record
Abstract
BACKGROUND: Femoral head collapse (FHC) is associated with a poor prognosis in osteonecrosis of the femoral head (ONFH). Preserved angles (PAs), including the lateral preserved angle (LPA), the anterior preserved angle (APA) and the combined preserved angle (CPA), can be used to quantify the extent of femoral head necrosis and predict the risk of femoral head collapse. The purpose of this retrospective cohort study was to assess the efficacy of these preserved angles in the prediction of femoral head collapse using plain radiographs. METHODS: Patients with ONFH treated conservatively between January 2010 and January 2019 were analyzed retrospectively to assess the risk of FHC. A logistic regression model was used to evaluate the independent prognostic factors associated with FHC, including age, sex, etiology, onset of symptom, The Japanese Investigation Committee classification, and PAs (LPA, APA, and CPA). RESULTS: A total of 137 patients, with 180 hips, had follow-up of at least two years and were included. During the follow-up period, FHC occurred in 89 hips (49.44%) after the initial diagnosis. Multivariable analysis indicated that CPA (odds ratio [OR] = 0.95; 95%CI = 0.93-0.97; P < 0.01) was a stronger predictor of femoral head collapse compared with the Japanese Investigation Committee classification (OR = 2.40, 95%CI = 0.92-6.25; P > 0.01). The receiver operating characteristic and survival curve analyses revealed that the predictive cutoff point for the CPA was 118.7° (sensitivity = 96.70%, specificity = 79.78%, log-rank test: P < 0.01). CONCLUSIONS: Assessment of preserved angles on plain radiographs is a simple method to quantify the extent of lateral and anterior necrosis of the femoral head. Specifically, CPA has a potential value in predicting femoral head collapse.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".