Correlation between dGEMRIC Index and other factors in assessment of early osteoarthritis in hip dysplasia
Bibliographic record
Abstract
[Objective]To assess early osteoarthritis in hip dysplasia using delayed gadolimium-enhanced magnetic resonance imaging of cartilage(dGEMRIC) technique and assess the correlation between this technique and other clinical factors. [Methods]Thirty-six hips in eighteen patients were treated in our hospital from Jan.2008 to Mar.2009.Clinical symptoms were assessed with use of the Western Ontario and McMaster Universities Osteoarthritis(WOMAC) questionnaire.The width of the joint space as well as the lateral center-edge angle of Wiberg was measured on standard standing AP pelvic radiographs.The correlation between the dGEMRIC index and other clinical factors was assessed using SPSS software.[Results]The dGEMRIC index was correlated with both pain(rs=-0.809,P0.000 1) and the lateral center-edge(rs=0.790,P0.000 1),but not the joint space width.The dGEMRIC index was significantly different among the three groups of mild,moderate,and severe dysplasia,whereas the joint space width did not differ significantly among the three groups.There was no significant correlation between age and any of the other parameters.[Conclusion]In patients with hip dysplasia,the dGEMRIC index may be an effective measure of early osteoarthritis.The dGEMRIC index correlates with pain and the severity of dysplasia and there is significant difference among the groups of mild,moderate,and severe dysplasia.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".