Predictors for remission in rheumatoid arthritis patients: A systematic review
Bibliographic record
Abstract
OBJECTIVE: To summarize the potential predictors of remission in patients with rheumatoid arthritis (RA). METHODS: We performed a systematic review of prognostic studies that identified the predictors of remission in RA patients. Studies were identified in Medline, EMBase, and the Cochrane Registry, and by hand search. We included only studies performing multivariate analysis. RESULTS: A total of 18 studies from 2,062 citations were included. The following variables were found to be the independent predictors of RA remission: male sex; young age; late-onset RA; short disease duration; nonsmoker; low baseline disease activity; mild functional impairment; low baseline radiographic damage; absence of rheumatoid factor and anti-citrullinated peptide; low serum level of acute-phase reactant, interleukin-2, and RANKL at baseline; MTHFR 677T alleles and 1298C alleles in the methotrexate (MTX)-treated patients; magnetization transfer ratio 2756A allele +/- either the SLC 19A180A allele or the TYMS 3R-del6 haplotype in the MTX plus sulfasalazine combination-treated patients; early treatment with nonbiologic disease-modifying antirheumatic drug (DMARD) combinations; the use of anti-tumor necrosis factor (anti-TNF); the concurrent use of DMARDs in anti-TNF-treated patients; and moderate or good response to treatments at the first 6 months. The magnitude of the association in the individual predictor was diverse among the studies depending on the patient characteristics, the study characteristics, and the variables used to adjust for in the models. CONCLUSION: A number of independent predictors of remission, i.e., baseline clinical and laboratory characteristics and genetic markers, were summarized. The predictive value of prognostic factors recently identified needs to be confirmed.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".