Dr. Kang, <i>et al,</i> reply
Bibliographic record
Abstract
To the Editor: We were surprised to read the letter by Berti and colleagues1, commenting on our recent article on the incidence of arterial and venous thrombosis in antineutrophil cytoplasmic antibody–associated vasculitis (AAV)2. They make the unjustified comment that “the incidence estimates for [arterial (ATE) and venous thrombosis events (VTE)] may be inflated” when we give clear and accurate incidence rates. Our incidence of ATE was 2.67/100 patient-years (PY), whereas the 2018 article by Berti, et al 3 reports an incidence of cardiovascular disease as 5.0/100 PY. It would therefore not seem that our incidence estimates were inflated. Our comparison of the characteristics of … Address correspondence to C.D. Pusey, Renal and Vascular Inflammation Section, Department of Medicine, Imperial College London, Hammersmith Campus, Du Cane Road, London W12 0NN, UK. E-mail: c.pusey{at}imperial.ac.uk
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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 | 0.020 |
| 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 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.022 | 0.026 |
| Insufficient payload (model declined to judge) | 0.007 | 0.007 |
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".