Bibliographic record
Abstract
BACKGROUND: Rheumatoid factors (RFs) are autoantibodies associated with rheumatoid arthritis. They can be detected in normal individuals, although transiently. This dichotomy has led to questions about the origins and types of RFs. Recently it has been shown that B cells that produce RFs only do so when activated by two signals, one from engagement of the B-cell receptor and the other from recognition of a pathogen-associated molecular pattern through a Toll-like receptor (TLR). These autoantibodies thus link the innate and acquired immune responses. OBJECTIVE: Through a review of the literature, an examination of the current knowledge of RF induction is presented. The focus is on a discussion of a beneficial or detrimental role for RFs in normal individuals and in those with chronic disease. RESULTS: What makes RF 'good' in some cases and 'bad' in others may reflect the type of RF produced. Low-affinity polyreactive IgM RFs are probably beneficial as they aid in the clearance of immune complexes that are more efficiently cleared, and the RF B cell can act as an antigen-presenting cell and stimulate host defense. However, large amounts of high-affinity RFs found in patients with chronic disease may be harmful by participation in a vicious cycle of autoantibody production by stimulation of self lymphocytes, and/or deposition in blood vessels thus causing vasculitis. CONCLUSIONS: Whether RFs are beneficial or detrimental depends on the context in which they are expressed, the type and amount of RF produced, whether the response is perpetuated by TLR ligation and whether other cells are stimulated either directly or indirectly by RF-positive B cells.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".