Bibliographic record
Abstract
In this issue of The Journal , Payet, et al examine a test for elevated anticyclic citrullinated peptide antibody (anti-CCP) levels and demonstrate its inability to identify rheumatoid arthritis (RA) among anti-CCP-positive patients with rheumatic disorders1. Their results seem to be at odds with previous findings, which have shown the high diagnostic accuracy of anti-CCP tests for differential diagnosis of RA2,3. It is important, therefore, to consider how to appropriately interpret studies of diagnostic accuracy and assess generalizability. Such considerations are important when planning, reporting, or reading studies of diagnostic accuracy. Pepe4 lists the following 6 criteria for identifying settings where diagnostic tests would be useful: (1) the disease should be potentially serious, (2) the disease should be relatively prevalent in the target population, (3) the disease should be treatable, (4) the treatment should be available to those who test positive, (5) the test should not harm the individual, and (6) the test should accurately classify diseased and non-diseased individuals. Given that RA is a chronic disease (with a worldwide prevalence of 1%5) that can lead to severe disability, premature mortality6, and a loss of quality of life7, and given that appropriate therapeutic intervention can greatly enhance clinical outcomes6, it is clear that the first 4 criteria have been met in this setting. Anti-CCP antibody tests satisfy the fifth criterion, so it remains to establish that they can be used to accurately classify diseased and non-diseased individuals, which motivates studies of diagnostic accuracy such as that considered by Payet, et al 1. Note that there is evidence that this sixth criterion could be met because anti-CCP tests have been shown to be useful in identifying patients with early-stage RA8 and predicting which patients will … Address correspondence to Dr. M.A. McIsaac, Public Health Sciences, Queen’s University, 99 University Ave., Kingston, Ontario K7L 3N6, Canada. E-mail: mcisaacm{at}queensu.ca
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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 | 0.529 | 0.919 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.010 |
| Bibliometrics | 0.037 | 0.021 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.022 | 0.015 |
| Open science | 0.010 | 0.013 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".