Bibliographic record
Abstract
Targeted therapeutic agents have changed the landscape of therapy in rheumatoid arthritis (RA). They have also provided valuable insights into the utility of animal models for development of targeted therapies, clinical trial design, pharmacodynamics, immunobiology and key pathogenic elements of disease. Studies of chimeric anti-CD4 monoclonal antibodies in RA demonstrated the need for pre-clinical studies to more closely approximate the human therapeutic paradigm as well as the importance of synovium as an appropriate pharmacodynamic window to predict efficacy and adverse side effects of the agents. Targeted therapies have been instructive in discerning the importance of TNF, IL-1, IL-6, IL-15 and RANKL in the pathological process themselves, such as the uncoupling of inflammation and structural damage. Current trends in the use of targeted therapeutics include aggressive earlier use, combination with methotrexate, use in moderate rather than severe disease, tight control as well as induration and maintenance regimes. Despite therapeutic advances with target therapies a number of unmet needs exist, including a low remission rate, cost and inadequate access as well as the lack of biomarkers to predict response and safety concerns. Despite this, target therapies have revolutionized the treatment of RA. In addition to having a substantial effect on clinical outcomes, a number of valuable lessons have been learned. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.018 | 0.013 |
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".