Fibrinogen albumin ratio as a predictor marker of disease activity in ankylosing spondylitis
Bibliographic record
Abstract
Abstract Background Fibrinogen albumin ratio (FAR) is recently considered as a new marker for inflammation. This study aimed to evaluate the value of FAR as a predictor biomarker of ankylosing spondylitis (AS) disease activity as well as its correlation with the spondyloarthritis research consortium of Canada MRI index (SPARCC) of sacroiliac joints (SIJs) in detection of disease activity in AS. The study included 25 adult AS patients and 10 healthy controls who were matched for age and sex. Based on disease activity, patients were divided into two groups: Group (I) consisted of 15 patients with active disease with a BASDAI ≥ 4, and group (II) consisted of 10 patients with inactive disease with a BASDAI < 4. Results The mean of patients age was (35.2 years); patient’s disease duration range (1.5–18 years). AS patients with disease activity had a higher FAR and SPARCC MRI index in comparison with the remission and control groups (P < 0.01). FAR and the SPARCC MRI index were found to be positively correlated in patients with active illness (P < 0.01). Cut-off value of FAR > 0.086, SPARCC index > 4. FAR and SPARCC MRI index can significantly differentiate between active and inactive group (P < 0.001) with 100% sensitivity and specificity. FAR can significantly differentiate between AS patients and health controls (P < 0.001), with 80% sensitivity and 100% specificity. SPARCC index also can significantly differentiate between AS patients and health controls (P < 0.001) but with 88% sensitivity and 100% specificity. Conclusions The study concluded that FAR and SPARCC MRI index were elevated in patients with active AS disease. FAR and SPARCC MRI index can serve as a new parameter in monitoring activity in ankylosing spondylitis disease.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".