Clinico‐sero‐pathological characteristics of anti‐Ha antisynthetase syndrome
Bibliographic record
Abstract
To define the clinical, serological, and muscle histopathological characteristics, as well as treatment outcomes, of patients with anti-Ha antibody. We performed a retrospective analysis of clinical, serological, and pathological data and long-term treatment outcomes of anti-Ha patients between January 2005 and July 2023 at our center. Anti-Ha antibody was identified by immunoblot and reconfirmed by immunoprecipitation. Of the 570 patients with idiopathic inflammatory myopathies, 17 (3.0%) were found to be anti-Ha positive, of whom 5 (29.4%) were also positive for another myositis-specific antibody (MSA). All patients with anti-Ha antibody as the single MSA (12/17, 70.6%) had clinical and histopathological evidence of muscle damage. Skin lesions were identified in nine of them (75%), while both interstitial lung disease and Raynaud's phenomenon were only seen in four patients. A necrotizing myopathy without a perifascicular pattern was the most common pathological manifestation (50%). Perifascicular necrosis (PFN) and myofiber major histocompatibility complex class-II expression were observed only in one and four patients, respectively. Muscle weakness relapse was reported in five patients, and skin rashes worsening were observed in one patient. Most of the anti-Ha patients (66.7%) finally achieved a favorable outcome at last follow-up. Anti-Ha antibody might not be as rare as previously thought and may coexist with other MSAs. Muscle damage is the most common manifestation in anti-Ha patients, while extra-muscular symptoms except for the cutaneous manifestations are unusual. The histopathological features varied with a predominance of necrotizing myopathy without PFN. These patients often finally had favorable outcomes, although relapses often occur.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".