Bibliographic record
Abstract
Effective treatment with etanercept results from a congregation of immunological signaling and modulating roles played by tumor necrosis factor-alpha (TNF-alpha), a pervasive member of the TNF super-family of cytokines participating in numerous immunologic and metabolic functions. Macrophages, lymphocytes and other cells produce TNF as part of the deregulated immune response resulting in psoriasis or other chronic inflammatory disorders. Tumor necrosis factor is also produced by macrophages and lymphocytes responding to foreign antigens as a primary response to potential infection. Interference with cytokine signaling by etanercept yields therapeutic response. At the same time, interference with cytokine signaling by etanercept exposes patients to potential adverse events. While the efficacy of etanercept for the treatment of psoriasis is evident, the risks of treatment continue to be defined. Of the potential serious adverse events, response to infection is the best characterized in terms of physiology, incidence, and management. Rare but serious events: activation of latent tuberculosis, multiple sclerosis, lymphoma, and others, have been observed but have questionable or yet to be defined association with therapeutic uses of etanercept. The safe use of etanercept for the treatment of psoriasis requires an appreciation of potential adverse events as well as screening and monitoring strategies designed to manage patient risk.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".