Bibliographic record
Abstract
To the Editor: We read with great interest the response by Drs. Jearn and Kim1 to our letter “Presence of Anti-topoisomerase I Antibody Alone May Not Be Sufficient for the Diagnosis of Systemic Sclerosis”2. We agree with Drs. Jearn and Kim that the antinuclear antibody (ANA)-negative accompanied by antitopoisomerase I antibody (anti-topo I) positivity is not sufficient to diagnose systemic sclerosis (SSc). Although we noted an apparent association between relatively lower anti-topo I antibody levels (median 76 AU/ml, range 42–118 AU/ml) and lung pathology in ANA-negative (6/11) or -positive (5/11) cases, we only suggested that our observation warrants further study. We did not state that the finding is clinically significant or that this pattern predicts pulmonary epithelial damage. Our article was written to inform clinicians on … Address correspondence to Dr. T.M. Frech, Department of Rheumatology, University of Utah, 4b200 SOM 30 N. 1900 E., Salt Lake City, Utah 84132, USA. E-mail: tracy.frech{at}hsc.utah.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 | 0.002 | 0.017 |
| 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 | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.020 | 0.023 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".