Novel MRI technique for the quantification of biochemical deterioration in steroid-induced osteonecrosis of femoral head: a prospective diagnostic trial
Bibliographic record
Abstract
Abstract To explore the novel magnetic resonance imaging techniques, IVIM-DWI and IDEAL-IQ in detecting bone marrow fat and microcirculation in steroid-induced osteonecrosis of the femoral head (SIONFH). In this prospective study, 49 patients (80 hips) with SIONFH taking glucocorticoids and 24 healthy volunteers (48 hips) were recruited and assessed by T1WI, T2WI/fs, IDEAL-IQ and IVIM-DWI. The affected hips, contralateral asymptomatic hips and normal hips, as well as normal, penumbra and necrotic areas in the affected hips, were classified and evaluated. Imaging results were compared with histologic bone sections obtained from SIONFH patients undergoing surgery. The fat fraction (FF) and perfusion fraction (f) differences between groups were analyzed using analysis of variance, the LSD t-test, Pearson correlation analysis and ROC curve analysis. Our results demonstrate that IDEAL-IQ (FF) and IVIM-DWI (f) enable the classification of SIONFH at different ARCO stages. The FF was positively associated with the progression of the disease (r = 0.72), in contrast to f (r = −0.17). The FF and f were significantly different among the necrotic, penumbra and normal areas, and they were negatively correlated with each other (r = −0.37). The diagnostic sensitivity and specificity of IDEAL-IQ were 96.9% and 86.7%, and those of IVIM-DWI were 72.34% and 58.33%, respectively. The FF in contralateral asymptomatic hips was significantly higher than in normal hips, but no difference was found for f. IDEAL-IQ, and not IVIM-DWI, was identified to successfully detect bone marrow fat, which is beneficial to the diagnosis of the severity of SIONFH.
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 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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".