Defining anti-synthetase syndrome: a systematic literature review
Bibliographic record
Abstract
OBJECTIVES: Anti-synthetase syndrome (ASSD) is a heterogeneous autoimmune disease characterised by multi-system involvement with a wide variety of manifestations. Validated classification criteria are necessary to improve recognition and prevent misclassification, especially given the lack of reliable and standardised autoantibody testing. We systematically reviewed the literature to analyse proposed ASSD criteria, characteristics, and diagnostic performance. METHODS: We searched PubMed and Embase databases (01/01/1984 to 06/11/2018) and the ACR and EULAR meeting abstracts (2017-2018). Sensitivities, specificities, positive, negative likelihood ratios and risk of bias were calculated for ASSD criteria and key variables reported in the literature. We performed meta-analysis when appropriate. RESULTS: We retrieved 4,358 studies. We found 85 proposed ASSD criteria from a total of 82 studies. All but one study included anti-synthetase autoantibody (ARS) positivity in the ASSD criteria. Most studies required only one ASSD feature plus anti-ARS to define ASSD (n=64, 78%), whereas 16 studies required more than one ASSD variable plus anti-ARS. The only criteria not including anti-ARS positivity required 5 ASSD clinical features. We found limited data and wide variability in the diagnostic performance of each variable and definition proposed in the literature. Given these limitations we only meta-analysed the performance of individual muscle biopsy and clinical variables in diagnosing ASSD, which performed poorly. CONCLUSIONS: The current ASSD criteria include a variety of serological, clinical, and histological features with wide variability amongst proposed definitions and the performance of these definitions has not been tested. This systematic literature review suggests the need for additional data and consensus-driven classification criteria for ASSD.
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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 | 0.011 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".