Bibliographic record
Abstract
Rheumatoid arthritis (RA) is a systemic disease we treat with medications that have systemic effects. It is becoming increasingly clear that patients with RA have accelerated atherosclerosis and that RA is an independent risk factor for the development of atherosclerotic coronary artery disease1. What is less clear is whether the presence of RA and its therapies modulates the course of coronary artery disease and its complications. In this issue of The Journal Myasoedova, et al use their extensive database from the Mayo Clinic to explore heart failure in patients with RA2. The relationship between heart failure and RA is not clear-cut. Some studies suggest that heart failure may be more frequent in patients with RA3, whereas other studies suggest the opposite4. Some of these differences can be explained by the more specific questions that the investigators are asking. Moreover, most studies of this problem are observational and depend on clinical diagnoses made during the course of clinical management, like the one in this issue. Thus, the presence or absence of heart failure is not systematically examined with questionnaires or echocardiograms, for example. This can be problematic in our effort to understand heart failure in RA patients since patients and physicians may ascribe symptoms such as ankle swelling as well as functional limitations to RA that may instead be a consequence of heart failure. Interestingly, … Address correspondence to Dr. Francis; E-mail: mark.francis{at}ttuhsc.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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".