Bibliographic record
Abstract
Just because nobody complains it doesn’t mean all parachutes are perfect. — Benny Hill While there has been an expansion in therapeutic targets and a renaissance of novel therapies in psoriatic arthritis (PsA)1, the efficacy and safety of tumor necrosis factor inhibitors (TNFi) in PsA have long been established2,3. With an influx of “cheaper” biosimilars attractive to regulators and reimbursers, it behooves rheumatologists spoiled with choice to maximize efficient use of readily accessible agents. Immunogenicity due to anti-drug antibodies (ADAb) in TNFi therapy has been shown to affect drug levels and subsequently to affect clinical response in rheumatoid arthritis (RA)4 and inflammatory bowel disease5. In turn it affects safety, inducing infusion and injection site reactions (ISR), but the issue has been less well studied in patients with PsA. Evidence from RA studies has shown that the combination of methotrexate (MTX) with TNFi reduces immunogenicity, significantly prolonging drug survival6. PsA data comparing monotherapy and combination therapy from Swedish and Consortium of Rheumatology Researchers of North America registries examining drug survival as a … Address correspondence to Dr. P. Nash, Griffith University, Medicine, PO Box 308, Sunshine Coast, Nathan, Queensland 4111, Australia. E-mail: drpnash{at}tpg.com.au
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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.022 | 0.027 |
| Insufficient payload (model declined to judge) | 0.012 | 0.011 |
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".