Bibliographic record
Abstract
To the Editor: We appreciate the thoughtful comments provided by Drs. Chen and Liu1, and Drs. Jearn and Kim2 in their respective letters in response to our manuscript3. They indeed provide helpful information and perspective in the interpretation of antinuclear antibody (ANA) testing and the reporting of cytoplasmic staining in laboratories outside of the United States. In principle, we all agree that it is helpful to recognize cytoplasmic staining patterns seen on HEp-2 ANA testing. Chen and Liu raise an important question about the definition of anticytoplasmic autoantibody (anti-CytAb) used in our study1. Our study simply focused on the potential recognition of antisynthetase antibody (anti-SynAb)–positive patients using the results of the screening ANA on HEp-2 cells. Our definition of positive anti-CytAb is cytoplasmic staining … Address correspondence to Dr. R. Aggarwal, UPMC Arthritis and Autoimmunity Center, Division of Rheumatology and Clinical Immunology, Department of Medicine, 3601 Fifth Ave., Suite 2B, Pittsburgh, Pennsylvania 15213, USA. E-mail: aggarwalr{at}upmc.edu, docrota{at}gmail.com
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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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.022 | 0.027 |
| 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".