A Negative Antinuclear Antibody Does Not Indicate Autoantibody Negativity in Myositis: Role of Anticytoplasmic Antibody as a Screening Test for Antisynthetase Syndrome
Bibliographic record
Abstract
OBJECTIVE: To evaluate the utility of anticytoplasmic autoantibody (anti-CytAb) in antisynthetase antibody-positive (anti-SynAb+) patients. METHODS: Anti-SynAb+ patients were evaluated for antinuclear antibody (ANA) and anti-CytAb [cytoplasmic staining on indirect immunofluorescence (IIF)] positivity. Anti-SynAb+ patients included those possessing anti-Jo1 and other antisynthetase autoantibodies. Control groups included scleroderma, systemic lupus erythematosus, Sjögren syndrome, rheumatoid arthritis, and healthy subjects. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy of anti-CytAb, and ANA were assessed. Anti-CytAb and ANA testing was done by IIF on human epithelial cell line 2, both reported on each serum sample without knowledge of the clinical diagnosis or final anti-SynAb results. RESULTS: Anti-SynAb+ patients (n = 202; Jo1, n = 122; non-Jo1, n = 80) between 1985-2013 with available serum samples were assessed. Anti-CytAb showed high sensitivity (72%), specificity (89%), NPV (95%), and accuracy (86%), but only modest PPV (54%) for anti-SynAb positivity. In contrast, ANA showed only modest sensitivity (50%) and poor specificity (6%), PPV (9%), NPV (41%), and accuracy (12%). Positive anti-CytAb was significantly greater in the anti-SynAb+ patients than ANA positivity (72% vs 50%, p < 0.001), and 81/99 (82%) ANA-negative patients in the anti-SynAb+ cohort had positive anti-CytAb. In contrast, the control groups showed high rates for ANA positivity (93.5%), but very low rates for anti-CytAb positivity (11.5%). Combining anti-CytAb or Jo1 positivity showed high sensitivity (92%) and specificity (89%) for identification of anti-SynAb+ patients. CONCLUSION: Assessing patients for anti-CytAb serves as an excellent screen for anti-SynAb+ patients using simple IIF. Cytoplasmic staining should be assessed and reported for patients suspected of having antisynthetase syndrome and a negative ANA should not be used to exclude this diagnosis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".