On Anti-Tumor Necrosis Factor-induced Systemic Lupus Erythematosus
Bibliographic record
Abstract
In the 1950s and 1960s Geritol tonic was heavily advertised on television as a fast cure-all if “you’re feeling weak and rundown, tired or nervous.” It was lampooned in a more recent sitcom as a magic elixir capable of transforming a friendless geek into the most popular kid at school. While the recent and numerous television advertisements for the anti-tumor necrosis factor (TNF) agents currently on the market do not make such outlandish claims as this, one cannot help but be awed by the overwhelming presence that these intravenously infused prescription drugs have so quickly established in the public sphere. TNF-α generates a vast array of biological properties, including cellular differentiation, proliferation, and apoptosis1–4. This variability is attributed to the binding of TNF to 2 distinct transmembrane receptors that can mediate TNF-induced inflammation and cell death, the promotion of proliferative responses in T lymphocytes and other hematopoietic cells5–7, and the induction of apoptosis in mature activated T cells8. Thus, TNF can serve dually as a potent proinflammatory mediator and a key immune modulator. Due to the multifarious effects of TNF, the results of either TNF administration or blockade have varied significantly from disease to disease and from animal models to human patients. In terms of TNF blockade, currently 3 anti-TNF agents are licensed for clinical use (the 2 monoclonal antibodies adalimumab and infliximab and a soluble TNF receptor, etanercept) and are prescribed for the treatment of rheumatoid arthritis (RA), juvenile RA, psoriatic arthritis, … Address correspondence to Dr. C.O. Jacob, University of Southern California, 2011 Zonal Ave., HMR 705, Los Angeles, CA 90033. E-mail: Jacob{at}usc.edu
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.038 | 0.032 |
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".