Reevaluating Serologic Markers of Poor Prognostic Factors in Rheumatoid Arthritis
Bibliographic record
Abstract
To the Editor: The article by Alemao, et al 1 documented that acceleration of treatment regimens for 3458 biologic-naive patients with rheumatoid arthritis (RA) did not significantly differ based on the presence or absence of poor prognostic factors (PPF). These findings suggest that, for whatever reason, clinicians were unable to translate PPF into a more aggressive therapeutic approach. Further, this occurred even though patients with a high initial PPF fared worse over a 12-month period in achieving low disease activity and maintaining employment. The reasons for this lack of translation by clinicians of PPF to more aggressive treatment is uncertain. The more recent focus on “treat to target,” evident in the 2015 American College of Rheumatology treatment recommendations, addresses high disease activity but does not directly address the role of other prognostic factors. This study suggests that treating to … Address correspondence to B. McEvilly, 1261 Liberty Way, Vista, California 92081, USA. E-mail: Mcevilly7{at}yahoo.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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.017 | 0.014 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".