Is It Good to Simplify Clinimetry in Chronic Inflammatory Joint Diseases?
Bibliographic record
Abstract
The measurement of disease activity in chronic inflammatory joint diseases represents a challenge that rheumatologists have faced head-on over the past decades. Disease activity is a complex phenomenon that, necessarily, must consider multiple domains of health. For some diseases outside the world of rheumatology, this task is somewhat facilitated. The evaluation of type II diabetes mellitus hinges on well-defined laboratory variables (eg, glycemia and glycated hemoglobin), and that of hypertension on instrumental values that are easily measured in a repeatable manner. For chronic inflammatory joint diseases (and beyond, such as connective tissue diseases or vasculitis, for example), the concept of disease activity integrates patient-reported measures, clinician-measured variables, and laboratory and instrumental tests. On the other hand, it must be this way, because these are conditions whose severity cannot be assessed by a single test also for methodological problems. To accomplish this task, rheumatologists invented composite indices of disease activity. The underpinnings of composite indices of disease activity are the ability of the index to be sensitive to change, predict disease evolution over time, and include all necessary variables in a nonredundant manner.1 The Disease Activity Score in 28 joints (DAS28), after more than 25 years since it was first validated, has been, and still is, one of the cornerstones of the assessment of patients with rheumatoid arthritis (RA).2 However, … Address correspondence to Dr. M. Di Carlo, Rheumatology Clinic, Università Politecnica delle Marche, “Carlo Urbani” Hospital, Via Aldo Moro, 25, 60035 - Jesi (Ancona), Italy. Email: dica.marco{at}yahoo.it.
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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 | 0.074 | 0.254 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.007 | 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".