Matching Therapy to Body Rhythms: An Endocrine Approach to Treating Rheumatoid Arthritis
Bibliographic record
Abstract
The value and efficacy of corticosteroids in treatment of rheumatoid arthritis (RA) has been recognized ever since the Nobel Prize in Physiology and Medicine was awarded in 1950 for the astonishing discovery of Edward Kendall, Philip Hench, and Tadeus Reichstein1. The description of Hench’s treatment of the first patient with Kendall’s compound E (later known as cortisone) reads like a story out of the annals of the miracle cures at Lourdes2. The young woman, unable to walk and bedridden with severe debilitating RA for 4 years, gets up, walks and leaves hospital recovered, only 4 days after treatment with daily intramuscular injections of the drug. The Nobel Prize was awarded to this team only one year after this observation, and after treatment of another couple of dozen patients3. Prednisone has since been a mainstay of treatment of RA and other inflammatory/autoimmune conditions. The major stumbling block for this otherwise miraculous drug has been its severe side effects when used in high doses and for prolonged periods of time. These include adrenal insufficiency; osteoporosis; metabolic syndrome, including diabetes; central fat deposition; skin atrophy; “moon” face; impaired resistance to infection, with increased … Address correspondence to Dr. Sternberg. E-mail: sternbee{at}mail.nih.gov
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".