Bibliographic record
Abstract
PURPOSE OF REVIEW: To assess the literature for biomarker validation studies that address key unmet needs related to the evaluation and management of patients with axial spondyloarthritis (SpA). This review focused on biomarkers facilitating early diagnosis and reflecting disease activity, structural damage on radiography, and clinical response to major therapies. RECENT FINDINGS: Early diagnosis may be facilitated by measurement of antibodies to the human leukocyte antigen class II-associated invariant chain peptide (anti-CD74) but sensitivity declines with increasing duration of disease. No disease activity biomarkers have demonstrated consistent superiority over standard C-reactive protein (CRP), and future validation should employ multivariate analysis aimed at demonstrating the added value of any associated biomarkers beyond available clinical parameters of disease activity and the use of magnetic resonance imaging inflammation as the primary endpoint. Several biomarkers reflecting inflammation (CRP and calprotectin), angiogenesis (vasoactive endothelial growth factor), and connective tissue turnover (C2 M, C3 M, and citrullinated metalloproteinase degraded fragment of vimentin ) have recently been shown to reflect radiographic progression in multivariate studies adjusted for baseline severity. Future studies should be prospective and demonstrate that predictive capacity adds to the information provided by known predictors such as CRP and baseline modified Stoke AS Spine Score. Calprotectin is a promising predictor of response to major therapies for axial SpA. SUMMARY: Several promising biomarkers addressing major unmet clinical needs require further validation in prospective studies.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".