Bibliographic record
Abstract
Biologics have greatly improved the management of rheumatoid arthritis (RA), demonstrating efficacy and safety in alleviating symptoms, inhibiting bone erosion, and preventing loss of function. Unmet therapeutic needs in RA remain; however, further advances require an understanding of issues left unaddressed under the current treatment paradigm. Most biologic-na?ve and biologic-pretreated patients who initiate a biologic therapy, for example, do not reach American College of Rheumatology 50% (ACR50) response, and few achieve remission. Responses are often not durable, prompting frequent treatment switching. Predictive markers are unavailable to guide therapy selection, and clinical trial data are lacking on whether a tumor necrosis factor inhibitor (TNFi) is the best first-line biologic and on the optimal sequence of use for the different biologics. Risk of serious infection is the major safety concern. Translating preclinical and clinical findings into new therapeutics may help address unmet needs. An increasing body of evidence indicates that the cytokine interleukin (IL)-17A represents an important therapeutic target; ongoing trials with IL-17A inhibitors will determine whether these agents can address some of the unmet needs associated with current biologics.
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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.033 | 0.006 |
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".