Bibliographic record
Abstract
Testing is an integral part of the diagnosis and management of patients with rheumatic disease. However, in this era of increased focus on healthcare costs, it is important to understand exactly how tests affect the delivery and costs of care. Along these lines, the American Board of Internal Medicine has begun the Choosing Wisely program with the goal “of advancing a national dialogue on avoiding wasteful or unnecessary medical tests, treatments and procedures”1. Multiple agencies in the United States and Canada are participating in this campaign, including the American College of Rheumatology (ACR), which has created a set “Five Things that Physicians and Patients Should Consider” in rheumatology-related testing2. These are important efforts, which hopefully will ultimately lead to improved clinical care, as well as improved costs of care, based on sound scientific investigations. Two tests that are used often in the evaluation of inflammatory arthritis (IA) are rheumatoid factor (RF) and anti-citrullinated protein antibodies (ACPA), with both of these tests being included in the 2010 ACR/European League Against Rheumatism (EULAR) classification criteria for rheumatoid arthritis (RA)3. Notably, these autoantibody tests are not included in the Choosing Wisely campaign. However, in the Canadian healthcare system, ACPA testing is not uniformly paid for, and as such a subset of patients with IA and RA do not have this testing performed. This has led to an opportunity for Shu and colleagues, whose report is published in the November 2015 issue of The Journal4 , to catch the spirit of the Choosing Wisely campaign and to explore the relationship between ACPA testing and the short-term management … Address correspondence to Dr. K.D. Deane, University of Colorado Denver, Anschutz Medical Campus, Division of Rheumatology, Department of Medicine, 1775 Aurora Court, Mail Stop B-115, Aurora, Colorado 80045, USA. E-mail: Kevin.Deane{at}UCDenver.edu
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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 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.015 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.018 | 0.019 |
| Insufficient payload (model declined to judge) | 0.016 | 0.008 |
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".