Bibliographic record
Abstract
To the Editor: We thank Dr. Muro and colleagues for the thoughtful analysis1 of our study on anti-DFS70 (anti-dense fine speckled 70) antibodies in systemic autoimmune rheumatic diseases (SARD), disease controls, and apparently healthy individuals as measured by a novel chemiluminescent immunoassay (CIA). The data on patients with dermatomyositis (DM) presented by Muro, et al are interesting and complement our findings2. Anti-DFS70 antibodies were found in 7/116 (6.4%) patients with DM. Although the prevalence in DM was not directly compared to a cohort of healthy individuals, based on previous data of 597 healthy hospital workers3, Muro and colleagues concluded that anti-DFS70 antibodies are less prevalent in persons with DM compared to healthy individuals (6.4% vs 10.7%, respectively). It is important to point out that the 2 cohorts were tested with 2 different ELISA systems, the DM cohort with a commercial ELISA and the healthy individuals with a research assay. Of high interest, the prevalence of isolated anti-DFS70 antibodies (with no other SARD-related autoantibody) was even lower. In the DM cohort, 2/116 … Address correspondence to Dr. Mahler; E-mail: mmahler{at}inovadx.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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.017 | 0.022 |
| Insufficient payload (model declined to judge) | 0.011 | 0.008 |
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".