Sacroiliitis in ankylosing spondylitis: correlations between marrow edema on MRI and clinical activity parameters
Bibliographic record
Abstract
Objective:To analyze the correlations among bone marrow edema of the sacroiliitis in ankylosing spondylitis(AS) and clinical activity parameters.Methods:Sixty-two patients with AS corresponding to the modified New York criteria were included.The patients were examined by MRI on sacroiliac joints(SIJs).All SIJs were scored according to the spondyloarthritis research consortium of Canada(SPARCC) for bone marrow edema.BASDAI score and laboratory tests including erythrocyte sedimentation rate(ESR) and C-reactive protein(CRP) were performed.Morning stiffness time was recorded.Correlative analysis was performed among SPARCC score,clinical data,and laboratory parameters.Results:The spearman correlation coefficient between SPARCC score and BASDAI score,morning stiffness time was 0.473 and 0.409(P0.05),and between SPARCC score and ESR,CRP was 0.094 and 0.179(P0.05).The spearman correlation coefficient between BASDAI score and morning stiffness time was 0.335(P0.05),and between CRP and ESR was 0.510(P0.05).No significant correlation was found between other data.Conclusion:In AS,the SPARCC score of SIJ bone marrow edema is positively correlated to the severity of BASDAI score and morning stiffness time,but there is no correlation between the SPARCC score and ESR,CRP.SPARCC score can be one of the bases to judge the activity of AS.
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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 | 0.001 | 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".