Anti-Tumor Necrosis Factor Agents Are Mostly Used in Patients with Established Rheumatoid Arthritis Compared to Early Disease — A Reflection of Adequate Clinical Practice
Bibliographic record
Abstract
Almost 10 years after the introduction of the anti-tumor necrosis factor (TNF) agents for the treatment of rheumatoid arthritis (RA), rheumatologists are still struggling with their appropriate use and especially with the time to start them in the disease course. It is now well accepted that earlier disease control, i.e., suppression of inflammation, translates into better outcomes in terms of halting radiographic damage progression and preventing functional disability. Several clinical trials have shown the superiority of earlier use of combination methotrexate (MTX) and an anti-TNF agent1–3 compared to either agent used alone. However, given the cost of these new agents, economic considerations and the absence of predictive markers of response have prevented their use as first-line agents. Several national and international guidelines and consensus statements on the use of anti-TNF agents have been published4, the latest being the American College of Rheumatology recommendation document5. Except for the European registries, which deal mostly with safety issues, there is little information on the practical use and effectiveness of anti-TNF agents, especially in early disease versus established RA. The article by Lee, … Address correspondence to Dr. B. Haraoui, CHUM, Campus Notre-Dame, 1560 Sherbrooke est, Montreal, Quebec H2L 1S6. E-mail: bharaoui{at}videotron.ca
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.010 |
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".