Seroconversion from Anti-Th/To to Anticentromere Antibodies in a Patient with Systemic Sclerosis
Bibliographic record
Abstract
To the Editor: Antinuclear antibodies (ANA) are involved in the diagnosis of systemic sclerosis (SSc), being present in 90% of the patients1,2,3. In the majority of ANA-positive patients with SSc, a single SSc-specific autoantibody can be detected, i.e. antibodies to centromere protein B (CENP-B)4, topoisomerase I5, RNA Pol-III3, and Th/To1,2,3,6,7. These 4 major autoantibodies are typically mutually exclusive. It has been found that anti-Rpp25 antibodies are an important target of antibodies to the Th/To complex6,7. Also, we discovered an epitope located on the Rpp38 subunit of the Th/To complex as the target of autoantibodies in patients with SSc. Anti-Th/To antibodies have been associated with the limited cutaneous form of SSc and with lung disease8. Case reports with sequential serum samples are valuable tools to study the evolution of autoantibody responses in autoimmune diseases9. We report a case of SSc with seroconversion from anti-Th/To to anti-CENP-B antibodies. The case report was approved by the Research Ethics Board under the … Address correspondence to Dr. M. Mahler, Vice President of Research, Inova Diagnostics Inc., 9900 Old Grove Road, San Diego, California 92131-1638, USA. 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".