Do patients with active RA have differences in disease activity and perceptions if anti-TNF naïve versus anti-TNF experienced? Baseline results of the optimization of adalimumab trial
Bibliographic record
Abstract
BACKGROUND: The chance of a good response in RA is attenuated in previous anti-TNF users who start new anti-TNF therapy compared to biologic naïve patients. In active RA, those with previous anti-TNF exposure compared to anti-TNF naïve may have different baseline disease activity and patient perceptions when starting a new anti-TNF treatment that could explain the observed response differences. MATERIAL/METHODS: The aim of this study was a post hoc analysis of baseline characteristics of patients enrolled in the Optimization of Adalimumab study that was a treat to target vs. routine care study in patients initiating adalimumab. As per the protocol, a maximum of 20% anti-TNF experienced patients were enrolled in the 300 patient trial. Twelve (4.0%) were excluded who previously used other biologics. Baseline characteristics including age, gender, tender and swollen joint counts, disease activity (DAS28), function (HAQ-DI), patient global assessment, patient satisfaction with current treatment, and inflammatory markers (CRP, ESR), were compared between previously anti-TNF experienced [etanercept or infliximab (EXP)], and anti-TNF naïve patients (NAÏVE). RESULTS: The mean (SD) age was 54.8 (13.3) years; 81.0% were female, and 237 (79.0%) were anti-TNF naïve while 51 (17.0%) patients were anti-TNF experienced (29 with etanercept, 16 with infliximab, and 6 for both). The mean (SD) baseline in EXP versus NAÏVE groups respectively was: CRP=21.7(32.9) vs. 17.5(20.7); ESR=28.7(22.5) vs. 29.8(20.4); SJC=10.5(6.0) vs. 10.7(5.6); TJC=12.8(7.1) vs. 12.3(7.3); and DAS28=6.0(1.2) vs. 5.8(1.1). None of the between-group differences were statistically significant, however, the HAQ-DI in EXP was 1.7(0.6) compared to 1.5(0.7) for the NAÏVE (P=0.021). Additionally, EXP patients had a higher patient global score [71.3(26.1) vs. 61.9(26.2), P=0.021]. CONCLUSIONS: Although anti-TNF naïve and experienced patients who initiated adalimumab were similar, with respect to several baseline characteristics, significant differences in subjective measures were observed, which may indicate more severe patient measures (function and global disease activity) in anti-TNF experienced patients.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".