Anti-TRIM72 Autoantibodies in Idiopathic Inflammatory Myopathies
Bibliographic record
Abstract
Background and Purpose-: . We hypothesized that IIM patients positive for anti-TRIM72 antibodies would have a more severe clinical phenotype. Methods-: Sera from IIM patient (antisynthetase syndrome [ASyS], immune mediated necrotizing myopathy [IMNM], and dermatomyositis [DM]) and healthy controls (HC) were included. Anti-TRIM72 autoantibodies were tested using enzyme linked immunosorbent assay. Anti-TRIM72 testing was positive if value was >2 standard deviations above the mean for HC. Clinicodemographic features were identified through chart review and compared between anti-TRIM72 positive (anti-TRIM72[+]) and negative (anti-TRIM72[-]) groups. Results-: Anti-TRIM72 levels were significantly increased in patients with ASyS and IMNM when compared to patients with DM and healthy controls. Anti-TRIM72 levels were also increased in patients expressing anti-Jo-1, anti-PL7, anti-HMGCR, anti-SRP, and anti-MDA5. In ASyS, when anti-TRIM72(+) and anti-TRIM72(-) patients were compared, there were significantly more anti-TRIM72(+) ASyS patients with normal DLCO (>75%) when compared to anti-TRIM72(-); however, there were no differences in demographic features, CK levels or FVC. In anti-HMGCR(+) IMNM, anti-TRIM72(+) was associated with a lower proportion of females, as well as older age at time of diagnosis and at time of anti-TRIM72 testing; however, there was no significant difference in other clinicodemographic features in anti-HMGCR(+) IMNM patients when anti-TRIM72(+) and anti-TRIM72(-) groups were compared. Conclusions-: Anti-TRIM72 antibody titres are increased in patients with ASyS and IMNM. The presence of anti-TRIM72 antibodies was not associated with a more severe phenotype in ASyS or anti-HMGCR(+) IMNM, and there were more ASyS patients with normal DLCO in the anti-TRIM72(+) group.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".