Bibliographic record
Abstract
Physicians rely on safety and efficacy data from pivotal trials to guide treatment decisions and manage patients. Even with robust clinical trial data, there remain questions regarding rare safety events and generalizability. Registries complement clinical trials. By evaluating effectiveness and safety in broad patient populations and often providing longer term or larger numbers of patients or both compared to clinical trials, registries consolidate and may extend the safety observations derived from pivotal trials. Our review of phase 3 clinical trial data, long-term extension studies and biologics registries shows biologics to be a safe option for short- and long-term use. Tumor necrosis factor (TNF)-, interleukin (IL)-12/23- and IL-17-antagonists yield similar safety profiles regarding infections, malignancy and major adverse cardiovascular events. The known risk of tuberculosis activation with TNF agonists appears to be readily handled by screening. Mild to moderate candida infections and potential exacerbation or de novo onset of inflammatory bowel disease are associated with IL-17 blockade.
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 | 0.056 | 0.148 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.055 | 0.030 |
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".