Bibliographic record
Abstract
Although initially approved for medical use in the United States by the Food and Drug Administration in the 1950s, antimalarial treatment of clinical disease actually dates back to the 1630s in Peru, stemming from the “fever tree,” later identified as Cinchona officialis in 1742 by Carl Linnaeus in Europe1. Later, quinine was isolated from Cinchona bark2, yielding the subsequent boom in the development of these agents for the antimalarial market. When the Dutch Cinchona plantations were overrun and captured during World War II, a synthetic version of quinine was created — quinacrine — and was used for malaria prevention, an activity funded and supported by the war effort in the United States3,4. The quinacrine story bears an uncanny similarity to the development of synthetic corticosteroids, which was also supported by the needs of the US government for the war effort during the exact same time period. It was in 1951, after the war was over, that Allied soldiers taking longterm quinacrine demonstrated improved signs and symptoms of systemic lupus erythematosus (SLE)5. Just a few years later (1955), hydroxychloroquine (HCQ) was synthesized, and a successful scale-up created this cornerstone drug for treating SLE. It is now on the World Health Organization list of essential medications needed in a basic health system6. In this issue of The Journal , Tselios, et al … Address correspondence to Dr. V.K. Sandhu, Loma Linda University Medical Center, Rheumatology, 11375 Campus St., Loma Linda, California 92354, USA. E-mail: vksandhu{at}llu.edu
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.006 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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; both teacher heads agree on what is shown here.
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".