Positive predictive value of myositis antibody line blot testing in patients with suspected idiopathic inflammatory myopathy
Bibliographic record
Abstract
INTRODUCTION/AIMS: Line blot (LB) is in widespread use for myositis antibody detection. Yet, studies of its positive predictive value (PPV) in patients with suspected idiopathic inflammatory myopathy (IIM), which would be of particular relevance to neuromuscular clinicians, are lacking. We aimed to determine the PPV of myositis antibody LB testing in patients with suspected IIM, and examine whether PPV was significantly impacted by intensity of antibody positivity. METHODS: This was a retrospective study of patients who underwent myositis antibody LB testing for suspected IIM between March 2019 and August 2022. RESULTS: Of 70 patients who underwent testing for suspected IIM and had positive myositis antibody LB results, 43 (61%) were female and the median age was 61 years (range: 10-83 years). Forty-four were classified as true-positives, yielding a PPV of 63%. The PPV of patients with weak-positive myositis antibody results (14/30, 47%) was significantly lower than the PPV of patients with moderate-positive or strong-positive myositis antibody results (30/40, 75%) (p = .02). DISCUSSION: Our study found that myositis antibody LB testing in patients with suspected IIM had a modest PPV, underscoring the need for antibody interpretation in the context of all available clinical and ancillary test data to avoid misdiagnosis. The significantly lower PPV in patients with weak-positive results emphasizes the particular importance of clinical correlation in such patients. Further study into the diagnostic performance of various LBs for myositis antibody detection is needed to inform their interpretation in clinical practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".