Bibliographic record
Abstract
I read with interest the editorial by Dr. Roy Fleischmann on the limitations of the multibiomarker disease activity test (MBDA) in the management of rheumatoid arthritis (RA)1. He provides a comprehensive review of some of the key studies of MBDA in clinical trials and concludes that the “MBDA has not met the test of being a biomarker that can predict who will or will not respond to a specific therapy, who will or will not be able to taper or stop therapy or who will or will not have radiographic progression.” While overall I tend to agree with him that this biomarker, along with ALL biomarkers, will fail to answer these questions in a binary fashion, I still disagree with his conclusions. Medicine in general and rheumatology in particular are professions immersed in diagnostic and therapeutic uncertainty. Rheumatologists share a ritual when confronting new patients with RA of appraising their disease activity and severity, … Address correspondence to Dr. L.H. Calabrese, Professor of Medicine, Cleveland Clinic Lerner College of Medicine of Case Western Reserve University 9500 Euclid Ave., Desk A50, Cleveland, Ohio 44195, USA. E-mail: CALABRL{at}ccf.org
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.028 | 0.168 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.012 | 0.020 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.020 | 0.051 |
| Insufficient payload (model declined to judge) | 0.012 | 0.011 |
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".