Frequency of serum autoantibodies to 21 autoantigens in 100 French Canadian patients with autoimmune myositis
Bibliographic record
Abstract
Copyright information:Taken from "Heterogeneity of autoantibodies in 100 patients with autoimmune myositis: insights into clinical features and outcomes"http://arthritis-research.com/content/9/4/R78Arthritis Research & Therapy 2007;9(4):R78-R78.Published online 9 Aug 2007PMCID:PMC2206383. Autoantibodies were observed to 19 (90%) of the specificities tested. Anti-OJ and anti-EJ (both anti-synthetases) were not detected. One or more autoantibodies were present in 80% of patients. Autoantibodies to synthetases (Jo-1, PL-7, PL-12, and KS) and systemic sclerosis autoantibodies were present overall in 22% and 9% of patients, respectively. The overall frequency is over 100% because 44% of patients had more than one autoantibody. Anti-Ro were determined by ALBIA whereas anti-Ro52 and anti-Ro60 fine specificities were identified by ELISA. See Materials and methods (in the text) for a description of immunoasssays. ALBIA, addressable laser bead immunoassay; CENP, centromere protein; ELISA, enzyme-linked immunosorbent assay; RNAPOLIII, RNA polymerase III; SRP, signal recognition particle; TOPO, topoisomerase I.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".