Anti-Sp4 and anti-CCAR1 autoantibodies in UK <i>vs</i> US patients with adult and juvenile-onset anti-TIF1γ-positive myositis
Bibliographic record
Abstract
OBJECTIVES: Anti-transcriptional intermediary factor 1γ (TIF1γ) autoantibodies are associated with malignancy in adult-onset idiopathic inflammatory myopathy (IIM) and this risk is attenuated if patients are also positive for anti-specificity protein 4 (Sp4) or anti-cell division cycle apoptosis regulator protein 1 (CCAR1). In anti-TIF1γ positive dermatomyositis (DM) patients from the USA, anti-Sp4 and anti-CCAR1 autoantibody frequencies are reported as 32% and 43% in adults and 9% and 19% in juveniles, respectively. This study aims to identify the frequency of anti-Sp4 and anti-CCAR1 in adult and juvenile UK anti-TIF1γ-positive myositis populations and report clinical associations. METHODS: Serum samples from 51 UK participants with adult-onset IIM and 55 UK participants with JDM, all anti-TIF1γ autoantibody positive, and 24 healthy control samples were screened for anti-Sp4 and anti-CCAR1 autoantibodies by ELISA. RESULTS: In UK adult anti-TIF1γ positive IIM patients, anti-Sp4 and anti-CCAR1 frequencies were 4% (2/51) and 16% (8/51). Both adult patients with anti-Sp4 were also positive for anti-CCAR1. In UK juveniles, anti-Sp4 was not detected and 13% (7/55) had anti-CCAR1 autoantibodies. Nineteen (37%) anti-TIF1γ positive UK adult myositis patients had cancer; neither of the two patients with anti-Sp4 autoantibodies and 25% (2/8) of anti-CCAR1 autoantibody-positive patients had cancer. No anti-Sp4 or anti-CCAR1 clinical associations were identified. CONCLUSION: Anti-Sp4 and anti-CCAR1 autoantibodies are less common in the adult UK anti-TIF1γ-positive myositis population compared with published data from the USA, limiting their use as biomarkers for cancer risk. In patients with juvenile onset disease, anti-Sp4 is less frequent in UK patients compared with the USA, but the prevalence of anti-CCAR1 autoantibodies is similar.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".