Response to Tumor Necrosis Factor Inhibitors in Rheumatoid Arthritis for Function and Pain is Affected by Rheumatoid Factor
Bibliographic record
Abstract
OBJECTIVES: To investigate differences in response to tumor necrosis factor inhibitor treatment (TNFi) in seropositive (rheumatoid factor positive; RF+) versus seronegative (RF-) patients with established RA as measured by the Health Assessment Questionnaire Disability Index (HAQ-DI) and pain. METHODS: RA patients from an established RA cohort were studied according to rheumatoid factor (RF) status for change in HAQ-DI and pain (0-3 VAS) one year after starting treatment with a TNFi. RESULTS: There were 238 patients treated with TNFi who had follow-up data (178 RF+ and 60 RF-). Disease duration was longer in RF+ vs RF- (12+8 vs 8+8 years) but the proportion of females (82% vs 72%, P=0.7), baseline HAQ-DI (1.44+0.63 vs 1.41+0.63, P=0.8) and pain (1.92+0.67 vs 1.93+0.67, P=0.9) were not different. The mean duration of treatment of first TNFi was 2.8 vs 2.3 years, P=0.1 and 68% of RF+ vs 62% of RF- were still receiving first TNFi at last visit (P=0.5). For patients with data at baseline and one year, the one-year HAQ-DI change was significantly greater in 90 RF+ patients (-0.356) versus 38 RF- patients (-0.126; P=0.04). The mean pain improvement was also greater in 77 RF+ vs 32 RF- patients (-0.725 vs -0.332 respectively; P=0.03). Numbers are small, data are missing and comorbidities, DAS28 and anti-CCP were not collected. CONCLUSION: Despite limitations in the data, in established RA after failure of DMARDs, RF+ patients may be more responsive to TNFi therapy as measured by changes in HAQ-DI and pain. INNOVATION: There may be a better response to TNFi in RA if RF positive for function and pain.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".