Bibliographic record
Abstract
We appreciate the feedback from Fritzler, et al 1, on our article “A Negative Antinuclear Antibody Does Not Indicate Autoantibody Negativity in Myositis: Role of Anticytoplasmic Antibody as a Screening Test for Antisynthetase Syndrome,” published in The Journal 2. Our primary goal was to simply call attention to the many clinical laboratories in the United States that will report a negative antinuclear antibody (ANA) and not report the presence of cytoplasmic staining, which could indicate the presence of an antisynthetase autoantibody. This indeed may be in the setting of other commercial autoantibodies (i.e., rheumatoid factor, antineutrophil cytoplasmic antibodies, etc.) also being reported as negative. The clinical correlate of this is that the patient with the antisynthetase syndrome (particularly those with non-Jo1 autoantibodies) may not fully manifest the entire clinical spectrum of the antisynthetase syndrome but could certainly present with lung dominant disease. Thus, when autoantibody testing is ordered on this subset of patients, there is the possibility that the report yields a “negative” ANA. This forme fruste of autoimmune interstitial lung … Address correspondence to Dr. R. Aggarwal, UPMC Arthritis and Autoimmunity Center, Division of Rheumatology and Clinical Immunology, Department of Medicine, University of Pittsburgh, 3601 Fifth Ave., Suite 2B, Pittsburgh, Pennsylvania 15213, USA. E-mail: aggarwalr{at}upmc.edu
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.033 |
| 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 | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.015 | 0.020 |
| Insufficient payload (model declined to judge) | 0.013 | 0.011 |
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".