Platelet Indices Are Associated with Disease Activity Scores and the Severity of Sacroiliitis on Magnetic Resonance Imaging in Axial Spondyloarthritis Patients
Bibliographic record
Abstract
Objective To investigate the associations among platelet indices, disease activity scores, and inflammatory markers in axial spondyloarthritis, and to determine the relation between platelet indices and inflammation measured on magnetic resonance imaging (MRI). Methods The study included 161 patients who fulfilled Assessment of Spondyloarthritis International Society criteria. Platelet indices such as mean platelet volume (MPV), plateletcrit (PCT), platelet large cell ratio (PLCR), and platelet distribution width (PDW) were measured. Ninety patients underwent sacroiliac (SI) MRI at baseline. Bone marrow edema (BME) and erosion on MRI were scored using the SPondyloArthritis Research Consortium of Canada (SPARCC) method. Bath Ankylosing Spondylitis Disease Activity Index (BASDAI), Bath Ankylosing Spondylitis Functional Index (BASFI), Ankylosing Spondylitis Disease Activity Score (ASDAS) and spinal radiologic progression were also assessed. The associations among platelet indices and disease activity scores and inflammatory markers were evaluated. Results Of the 161 patients, 130 (81%) were male. MPV, PLCR, and PDW were negatively associated with ASDAS and inflammatory marker expression, whereas PCT was positively associated with these parameters. MPV, PLCR, and PDW were negatively associated with BME and erosion scores on SI MRI. However, platelet indices were not associated with the BASDAI and BASFI. The mean erythrocyte sedimentation rate, C-reactive protein, and BME and erosion scores were significantly higher in patients with low MPV. Changes in MPV, PCT, and PDW at baseline and after one year were associated with changes in ASDAS and inflammatory marker expression. Conclusion Platelet indices are associated with ASDAS, inflammatory marker levels, and severity of BME and erosion measured on MRI.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".