Dr. Dessein, et al reply
Bibliographic record
Abstract
To the Editor: We thank Dr. Makrilakis and colleagues for their interest1 in our study of 236 patients with rheumatoid arthritis (RA) that documented a potential role of the adipokine chemerin in atherogenesis and cardiovascular disease (CVD) risk stratification2. Dr. Makrilakis, et al 1 reported on their own recent findings that interleukin 6 (IL-6) inhibition with tocilizumab (TCZ) in RA resulted in reduced chemerin concentrations, as well as decreased plasminogen activator inhibitor 1 levels (PAI-1)3 and arterial stiffness as estimated by the carotid-femoral pulse wave velocity (PWV)1; reductions in chemerin concentrations were associated with those of PAI-1 levels3 and decreases in carotid-femoral PWV1. In our investigation2, we had shown that not only excess was adiposity, but also disease activity and inflammatory markers were strongly related to chemerin concentrations. Taken together, our findings and those in the Makrilakis, et al study support the notion that chemerin may contribute to the reported link between inflammation and enhanced CVD risk in RA. Further, whereas greater … Address correspondence to Dr. P.H. Dessein, P.O. Box 1012, Melville 2109, Johannesburg, South Africa. E-mail: dessein{at}telkomsa.net
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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 | 0.016 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.020 | 0.018 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".