Evidence for treating rheumatoid arthritis to target: results of a systematic literature search update
Bibliographic record
Abstract
OBJECTIVE: A systematic literature review (SLR; 2009-2014) to compare a target-oriented approach with routine management in the treatment of rheumatoid arthritis (RA) to allow an update of the treat-to-target recommendations. METHODS: Two SLRs focused on clinical trials employing a treatment approach targeting a specific clinical outcome were performed. In addition to testing clinical, functional and/or structural changes as endpoints, comorbidities, cardiovascular risk, work productivity and education as well as patient self-assessment were investigated. The searches covered MEDLINE, EMBASE, Cochrane databases and Clinicaltrial.gov for the period between 2009 and 2012 and separately for the period of 2012 to May of 2014. RESULTS: Of 8442 citations retrieved in the two SLRs, 176 articles underwent full-text review. According to predefined inclusion/exclusion criteria, six articles were included of which five showed superiority of a targeted treatment approach aiming at least at low-disease activity versus routine care; in addition, publications providing supportive evidence were also incorporated that aside from expanding the evidence provided by the above six publications allowed concluding that a target-oriented approach leads to less comorbidities and cardiovascular risk and better work productivity than conventional care. CONCLUSIONS: The current study expands the evidence that targeting low-disease activity or remission in the management of RA conveys better outcomes than routine care.
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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 | 0.015 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.022 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".