<i>Quantitative Clinical Rheumatology:</i> “Keep It Simple, Stupid”: MDHAQ Function, Pain, Global, and RAPID3 Quantitative Scores to Improve and Document the Quality of Rheumatologic Care
Bibliographic record
Abstract
> “The KISS principle (acronym for “Keep It Simple, Stupid”) states that design simplicity should be a key goal and unnecessary complexity avoided.... Extra features are not needed; an approach that seems “too easy to be true” is in fact the best way.” The KISS principle (“Keep It Simple, Stupid”) has been applied effectively in many disciplines, including “software development, animation, photography, engineering, and strategic planning” (Wikipedia). In clinical medicine, perhaps the most elegant example of the KISS principle involves simple laboratory tests such as hemoglobin, creatinine, etc., that may be applied to diagnosis, prognosis, management, and documentation of outcomes of diseases. The discovery of rheumatoid factor (RF)1 and antinuclear antibodies (ANA)2 in 1949 raised hope that similar, simple biomarkers could be applied to diagnosis, prognosis, and management of inflammatory rheumatic diseases. Laboratory biomarkers provide an invaluable foundation to understand pathogenesis and develop new therapies, such as biological agents, and are informative in groups of patients. Nonetheless, laboratory tests have many limitations in the care of individual patients with rheumatic diseases. For example, in patients with rheumatoid arthritis (RA), RF is positive in only 69%3, anti-cyclic citrullinated peptide (anti-CCP) antibodies are positive in only 67%3, and erythrocyte sedimentation rate (ESR) is greater than 28 mm/h in only 60%4. Only about 1 in 50–100 people with … Address reprint requests to Dr. T. Pincus, NYU Hospital for Joint Diseases, 301 East 17th Street, New York, NY 10003. 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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.139 | 0.109 |
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".