A Rheumatologist Managing Patients with Rheumatoid Arthritis: An Artisan But Also An Artist!
Bibliographic record
Abstract
In this issue of The Journal Lonnie Pyne and colleagues report the results of an analysis aimed at evaluating the respective roles of the patient (patient’s global assessment), the physician (physician’s global assessment), and a composite index, the Disease Activity Score (DAS)1 in the decision for indicating and/or reinforcing a disease-modifying drug in rheumatoid arthritis (RA) in daily practice in Canada2. For this purpose, they took the opportunity to use data collected in the CATCH study (the Canadian Early Arthritis Cohort). The main conclusion of this elegantly conducted analysis is that the increase of treatment was strongly related to the physician’s global assessment, whereas DAS28 was not. The results have to be interpreted with regard to the following new paradigms in the management of RA, in particular at the early stage of the disease. 1. The current main objective of therapy with a disease-modifying antirheumatic drug (DMARD) in early RA is not only to improve the current symptomatic condition of the patient (e.g., level of pain, functional impairment, fatigue) but also to prevent any subsequent clinical handicap due to structural damage. Inflammation has been shown in different longitudinal epidemiological … Address correspondence to Prof. Dougados; E-mail: m.doug{at}cch.aphp.fr
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.015 | 0.019 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".