DWI and SPARCC scoring assess curative effect of early ankylosing spondylitis
Bibliographic record
Abstract
BACKGROUND: To investigate the magnetic diffusion weighted imaging (DWI) sequence and Spondyloarthritis Research Consortium of Canada (SPARCC) scoring in assessing curative effect of combined treatment of Chinese and Western medicine for early ankylosing spondylitis (AS). METHODS: 48 cases diagnosed as early AS and treated with Chinese and Western medicine were included in the study. Magnetic routine and DWI sequence scanning image were performed to obtain the mean apparent diffusion coefficient (ADC) value of sub-articular surface bone marrow. Combined with SPARCC scoring, statistical analysis was conducted to compare the difference with the information obtained in the previous study. RESULTS: The mean ADC value in the sub-articular surface bone marrow of patients after clinical treatment: (4.34±0.55)×10-4mm2/s in ilium and (3.96±0.23)×10-4mm2/s in sacrum, which were both significantly lower than that before treatment (p< 0.05). There was highly positive correlation between mean ADC value and SPARCC scoring (P<0.05). The regression relationship could be demonstrated as Y=-64.420+21.262X(Y: SPARCC scoring value; X: mean ADC value). CONCLUSIONS: Magnetic DWI and SPARCC scoring could be applied in accessing AS inflammation activity changes and in reflect of curative effect of early AS patients as well as in providing reliable radiologist evidence for clinical therapeutic efficacy.
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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 | 0.001 |
| 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".