Anti-tRNA Synthetase-specific Immunofluorescence Patterns are Easily Detected in the Suitable Substrate
Bibliographic record
Abstract
To the Editor: We read with interest the article by Aggarwal, et al 1. We agree with the authors when they evoke the importance of cytoplasmic staining for early diagnosis of myositis while refraining from reporting an “antinuclear antibody (ANA)–negative result.” However, we disagree with reporting it as a generic concept without its specific description, because as noted by the authors, cytoplasmic staining preserves various patterns, and we should mind the possible diffusion of SS-A/Ro into the cytoplasm during the fixation process of HEp-2 substrate. Besides the number of pattern types and the diversity of target antigens, the frequency with which they are experienced in laboratories should be considered as another important factor. Under the real circumstance of cytoplasmic staining on HEp-2 cells, we usually encounter anti-Ro, which has intensely higher incidence than anti-tRNA synthetase. We have also frequently observed antimitochondrial antibodies. Moreover, even other patterns, the clinical significance of which is still less known, are … Address correspondence to Dr. T.Y. Kim, Department of Diagnostic Immunology/Laboratory Medicine, Hanyang University Medical Center, 222-1 Wangsimni-ro, Seongdong-gu, Seoul, 04763, Republic of Korea. E-mail: tykim{at}hanyang.ac.kr
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".