Influence on Effectiveness of Early Treatment with Anti-TNF Therapy in Rheumatoid Arthritis
Bibliographic record
Abstract
PURPOSE: To evaluate the association between starting early treatment with anti-TNF and effectiveness as well as the possibility of applying therapeutic spacing in daily practice in patients with rheumatoid arthritis (RA). METHODS: Observational, retrospective study conducted in two universitary hospitals in Spain. RA patients who received the first anti-TNF (adalimumab: ADA, etanercept: ETN or infliximab: IFX) during the study period (October 2006-2010) were included. Demographic data, time since diagnosis, disease activity (DAS28-ESR) and anti-TNF dosage were analyzed. Therapeutic objective was defined as DAS28 DAS28 < 2.6. Also the response related to criteria of the European League Against Rheumatism (EULAR) was evaluated. Therapeutic spacing was defined as the use of a lower dose or a higher interval according to label doses. The main endpoint was to assess the association between the effectiveness and the moment when the anti-TNF therapy begins. The secondary target was to evaluate the association between RA activity at the beginning of treatment with anti-TNF and dose used. Results. 82 patients were included. The prescription profile was: ADA (48.8%), ETN (31.7%) and IFX (19.5%). 71.4% of patients treated with anti-TNF during the first year since diagnosis, 57.1% of those who started after 1-5 years and 30.6% of patients who started after 5 years were in remission when the study ended. De-escalation strategy was performed in 25.6% of patients: ETN (38.5%), ADA (20.0%) and IFX (18.8%). The patients treated with a higher dose according to label doses were: IFX (81%), ADA, (12.5%) and ETN (7.7%). CONCLUSIONS: Results suggest that early treatment with anti-TNF can achieve a higher percentage of remissions. Therapeutic spacing is established as a strategy that improves the efficiency in those patients in remission, being the ETN the anti-TNF most susceptible for spacing, although a relation between the early beginning with anti-TNF and the used dose was not found.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".