Improving rheumatoid arthritis outcomes: How do we get there?
Bibliographic record
Abstract
The symposium concluded with a panel discussion that reprised major points and provided additional insights. J. Richard Brown, Pharm.D., BCPS, FASHP, symposium chair, served as moderator. Highlights of the discussion are presented below. Patients with rheumatoid arthritis (RA) often have sicca symptoms that are very difficult to deal with, especially dry mouth and the Sjögren’s syndrome-like presentation. Is there any treatment that can be offered to these patients? There are some artificial local agents and muscarinic agonists, such as pilocarpine and cevimeline, that can stimulate saliva production, but that’s about all I’m aware of for those symptoms. Any orifice with a mucous membrane—the eyes, nose, mouth, vagina—as well as the skin, can be affected by sicca syndrome, and symptoms can be significant. Conjunctivitis can be a problem sometimes, and it requires topical treatment. Unfortunately, there haven’t been any major medical breakthroughs; therefore, the primary treatment goal is symptom management. Researchers at the National Institutes of Health are studying biopsy specimens of salivary glands to determine if there are any potential therapeutic targets, but results are not yet available.
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 | 0.044 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.024 | 0.031 |
| Insufficient payload (model declined to judge) | 0.022 | 0.007 |
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".