What Can We Tell Our Patients About Rheumatoid Arthritis Risk?
Bibliographic record
Abstract
You are seeing a 45-year-old female with a chief complaint of joint pain in the hands and feet. The symptoms have been apparent for 6 months. There was no preceding illness. She reports morning stiffness of the affected joints. The patient denies any joint swelling. Her medical history is notable for a strong family history of Rheumatoid Arthritis, and she currently smokes one pack of cigarettes daily. On physical examination, the joints appear normal, with full range of motion and no obvious tenderness to palpation. There is no evidence of synovitis or rashes. Laboratory investigations show elevated anti-citrullinated protein antibody level of 135, and her rheumatoid factor level is 45. C-reactive protein is within normal limits. Radiographs of the hands and feet are normal. Questions: 1. What is her likelihood of developing rheumatoid arthritis within the next 3 years? 2. Are there any other tests you need to order? 3. Can rheumatoid arthritis be prevented in this individual? What advice can you provide her?
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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 | 0.002 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.013 | 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".