Evaluating the Real-world Benefits and Risks of Anti-Tumor Necrosis Factor Therapies
Bibliographic record
Abstract
The discovery of tumor necrosis factor-α (TNF-α) as the pivotal cytokine in the inflammatory pathway and the development of drugs specifically targeted at this molecule has revolutionized our treatment of patients with rheumatoid arthritis (RA) and other inflammatory arthritides over the past decade. Trial after trial, investigators have consistently demonstrated efficacy in reducing disease activity, inflammation, and radiographic progression with anti-TNF therapy1. However, questions remain regarding the longterm safety of anti-TNF therapy because of the pleiotropic effects of TNF-α in immune system regulation. Such questions cannot always be answered in the context of a randomized controlled trial (RCT). By its very nature, an RCT is an experiment, an attempt to replicate a controlled laboratory environment using a homogeneous population of study subjects, with preset questions. This limits the generalizability of the results to the wider population of patients we see in routine clinical practice2. Also, some questions, especially about longterm safety, cannot always be answered because the duration of an RCT is relatively short. Observational studies can go some way toward answering these pertinent questions. Over the past decade, a number of biologic registers and other observational studies of “real-world” anti-TNF use have been established and have started to address important questions regarding treatment benefits3,4, longterm treatment persistence5, and safety, with a focus on serious infections6,7,8 and malignancy9,10,11. Many of these studies have been based in large healthcare claims databases, which gather details about patient interactions with the healthcare system12. These studies have the advantage of large sample sizes but can be limited in the detail … Address correspondence to Dr. Hyrich; E-mail: kimme.hyrich{at}manchester.ac.uk
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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 | 0.095 | 0.233 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".