Documenting the Value of Care for Rheumatoid Arthritis, Analogous to Hypertension, Diabetes, and Hyperlipidemia: Is Control of Individual Patient Self-Report Measures of Global Estimate and Physical Function More Valuable Than Laboratory Tests, Radiographs, Indices, or Remission Criteria?
Bibliographic record
Abstract
Recent recommendations for treatment of rheumatoid arthritis (RA) include “treat-to-target” with a “primary target…a state of clinical remission”1. Remission is now a realistic target in RA, because patient status is substantially better than in previous decades in most developed countries2. Capacity to induce remission may be an effective rationale for support of aggressive treatment with expensive therapies to insurance company and government payers. The concept of “treat-to-target” was developed over the years in other chronic diseases, notably hypertension3,4, diabetes5, and hyperlipidemia6. The basis of treat-to-target was not “remission,” a state that may often be possible, but as in RA, usually requires continued lifelong medication. The target, in other diseases, involves “tight control” of a “gold standard” biomarker of dysregulation — elevated blood pressure, serum glucose, or serum cholesterol — to a lower level that results in improved quality of life and reduction of premature mortality rates. Such a target – not a state of remission – provides a strong rationale for aggressive treatment. RA differs substantially from hypertension, diabetes, or hyperlipidemia in that there is no single, gold standard biomarker (or any other measure) for diagnosis, management, or prognosis in all individual patients. Biomarkers are of unquestioned importance in RA to understand pathogenesis and develop new therapies: Biological agents would not be available without them. However, biomarkers are limited in clinical application to diagnosis, management, and prognosis of RA: Forty percent of new patients have normal erythrocyte sedimentation rate (ESR) or C-reactive protein (CRP)7,8, and > 30% test negative for rheumatoid factor or anti-citrullinated protein antibodies (ACPA)9. Clinical decisions in RA are based more on a patient history and physical examination than on biomarkers; in contrast, biomarkers dominate clinical decisions in many … Address correspondence to Dr. Pincus; E-mail: tedpincus{at}gmail.com
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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 | 0.028 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".