Dose adjustments and discontinuation in TNF inhibitors treated patients: when and how. A systematic review of literature
Bibliographic record
Abstract
Objectives: To review the available evidence concerning the possibility of discontinuing and/or tapering the dosage of TNF inhibitors (TNFi) in RA patients experiencing clinical remission or low disease activity. Methods: A systematic review of the literature concerning the low dosage and discontinuation of TNFi in disease-controlled RA patients was performed by evaluation of reports published in indexed international journals (Medline via PubMed, EMBASE), in the time frame from 8 April 2013 to 15 January 2016. Results: We analysed the literature evaluating the efficacy and the safety of two different strategies using TNFi, decreasing dosage or discontinuation, in patients experiencing clinical remission or low disease activity. After the analysis of online databases, 25 references were considered potentially relevant and 16 references were selected. The majority of data concerned etanercept and adalimumab. Results suggested the induction of stable clinical remission or low disease activity by using TNFi followed by a dosage tapering and/or discontinuation of such drugs may be associated with the maintenance of a good clinical response in a subset of patients affected by early disease. Conclusion: RA patients treated early with TNFi and achieving their therapeutic clinical targets seem to maintain their clinical response after tapering or discontinuing TNFi. These data may allow physicians a more dynamic and tailored management of RA 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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".