Clinical value of magnetic resonance imaging graded by SPARCC and HLA-B27 in diagnosing early ankylosing spondylitis
Bibliographic record
Abstract
Objective To study the clinical value of magnetic resonance imaging(MRI) in low fields on sacroiliac joints(SIJs) and human leukocyte antigen(HLA)-B27 in diagnosing early ankylosing spondylitis(AS). Methods Forty patients with AS corresponding to the modified New York criteria were included.The HLA-B27 was measured,and MRI in low fields on SIJs was examined in all patients.The results of MRI were scored according to the spondyloarthritis research consortium of Canada(SPARCC),and the correlative analysis between SPARCC score of MRI and Bath AS disease activity index(BASDAI) score in clinic was performed. Results The positive and negative expressions of HLA-B27 were 35 cases(87.5%) and 5 cases(12.5%),respectively.There was no significant difference in SPARCC score between HLA-B27 negative group(18.30±9.78)and positive group(12.40±4.86)(t=1.709,P=0.105).The SPARCC score in active period was higher than that in quiescent period(17.14±3.80 vs 5.58±2.40,t=8.521,P=0.000).Pearson correlative analysis showed that SPARCC score was positively correlated to the BASDAI score(r=0.675,P0.05),and demonstrated that there was a better correlation in two score systems. Conclusions SPARCC score of MRI on SIJs could provide important evidence for diagnosing the early AS.HLA-B27 detection must rely on imaging condition as foundation in order to display its effect.
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 | 0.001 | 0.004 |
| 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".