Factors Associated With Sustained Remission in Rheumatoid Arthritis in Patients Treated With Anti–Tumor Necrosis Factor
Bibliographic record
Abstract
OBJECTIVE: Anti-tumor necrosis factor (anti-TNF) antibody has revolutionized the treatment of rheumatoid arthritis (RA), and remission is now a realistic possibility for patients. Despite widespread use of anti-TNFs, predicting which patients are most likely to attain a sustained good response to these treatments remains challenging. Our objective was to undertake a systematic review of the literature to evaluate existing evidence for demographic and clinical factors associated with the achievement of sustained remission in individuals with RA treated with anti-TNF therapy. METHODS: Embase, Medline, and the Cochrane Controlled Trials Register were searched along with studies identified from reference lists. Quality of studies was assessed using Newcastle-Ottawa criteria. Meta-analysis was undertaken where unadjusted odds ratios were available for the same demographic or clinical factors from at least 3 studies. RESULTS: Six studies were identified. Concomitant methotrexate use was associated with an increased likelihood of achieving sustained remission. Greater baseline disease activity, tender joint count, age, disease duration, baseline functional impairment, and female sex were associated with reduced likelihood of achieving sustained remission. CONCLUSION: Factors predicting sustained remission are seldom reported. Evidence identified in this review supports current recommendations for methotrexate coprescription and highlights the negative impact of particular clinical and demographic features on the likelihood of achieving optimal response to anti-TNF treatment. Sustained remission is clinically more relevant than point remission in RA. More widespread reporting of sustained remission will help clinicians set realistic expectations on likely long-term treatment efficacy and could be an important tool for identifying patients suitable for dose optimization.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.004 | 0.006 |
| 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) | 0.002 | 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".