Bibliographic record
Abstract
Advances in science have offered opportunities to develop targeted therapies for multiple disease states. Physicians and patients are becoming accustomed, and even expect, to use targeted therapies, avoiding any “shotgun approaches.” The discovery of biologics has revolutionized the treatment of rheumatological diseases. The benefits have greatly outweighed the risks with biologic therapy, and have subsequently reduced tender and swollen joints, improved quality of life, decreased morbidity, and slowed disease progression in this population as a whole1. But despite our greater understanding of the physiology of the inflammatory process of gout, our approach to treatment has remained antiquated. The approved US Food and Drug Administration (FDA) therapies for gout flares include indomethacin, naproxen, sulindac, corticosteroids, and colchicine2,3,4,5. Nonsteroidal antiinflammatory drugs (NSAID) have been used to treat gout flares since the 1960s, corticosteroids since the 1950s, and documented use of colchicine goes back to 17632,6. The efficacy of all these medications for the treatment of gout flares is well documented, but not all patients respond adequately; and in a population that is likely to have 1 or more comorbid conditions, these FDA-approved medications can carry significant risks and adverse effects7. In this issue of The Journal , Desmarais and Chu evaluate the efficacy and safety of anakinra, a biologic therapy that targets interleukin (IL)-1 receptors, thereby blocking IL-1 activity, a major driver of inflammation in an acute gout flare8. The authors used retrospective data spanning almost 9 years from the Oregon Health & Science University (OHSU) Hospital and the Veteran’s Administration Portland Health Care System (VAPORHCS) to identify hospitalized patients who carried a diagnosis of gout or calcium pyrophosphate (CPP) deposition, who flared, and who had received at least one 100-mg dose of anakinra … Address correspondence to Dr. R.T. Keenan, Duke University School of Medicine, 200 Trent Drive, DUMC 3544, Durham, North Carolina 27710, USA. E-mail: Robert.keenan{at}duke.edu
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.030 | 0.008 |
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".