Biochemical markers of ongoing joint damage in rheumatoid arthritis - current and future applications, limitations and opportunities
Bibliographic record
Abstract
Rheumatoid arthritis (RA) is a chronic systemic autoimmune disease associated with potentially debilitating joint inflammation, as well as altered skeletal bone metabolism and co-morbid conditions. Early diagnosis and aggressive treatment to control disease activity offers the highest likelihood of preserving function and preventing disability. Joint inflammation is characterized by synovitis, osteitis, and/or peri-articular osteopenia, often accompanied by development of subchondral bone erosions, as well as progressive joint space narrowing. Biochemical markers of joint cartilage and bone degradation may enable timely detection and assessment of ongoing joint damage, and their use in facilitating treatment strategies is under investigation. Early detection of joint damage may be assisted by the characterization of biochemical markers that identify patients whose joint damage is progressing rapidly and who are thus most in need of aggressive treatment, and that, alone or in combination, identify those individuals who are likely to respond best to a potential treatment, both in terms of limiting joint damage and relieving symptoms. The aims of this review are to describe currently available biochemical markers of joint metabolism in relation to the pathobiology of joint damage and systemic bone loss in RA; to assess the limitations of, and need for additional, novel biochemical markers in RA and other rheumatic diseases, and the strategies used for assay development; and to examine the feasibility of advancement of personalized health care using biochemical markers to select therapeutic agents to which a patient is most likely to respond.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".