Testing for Acetylcholine Receptor and Muscle‐Specific Tyrosine Kinase Antibodies by Fixed Cell‐Based Assay in Clinical Practice: Positive Predictive Value for Myasthenia Gravis
Bibliographic record
Abstract
BACKGROUND: Fixed cell-based assays (CBAs) to detect acetylcholine receptor and muscle-specific tyrosine kinase antibodies (anti-AChR/MuSK) are now available, but evaluations of their diagnostic performance in clinical practice are lacking. We examined the positive predictive value (PPV) of anti-AChR/MuSK fixed CBA for myasthenia gravis (MG), following the implementation of this assay as first-line testing at our centre. METHODS: We identified all patients at our centre with positive anti-AChR/MuSK fixed CBA results between November 2021 and July 2024. Clinical information was reviewed to classify patients as having true positive or false positive antibody results. Patients with a clinical presentation compatible with MG and no more likely alternative diagnosis were classified as true positives, whereas all others were classified as false positives. Test PPV was calculated as the proportion of positives that were classified as true positives. RESULTS: Of 770 patients who underwent anti-AChR/MuSK fixed CBA testing, 109 (14%) had positive antibody results (Anti-AChR, 105; Anti-MuSK, 4). Among them, one patient with anti-AChR positivity was classified as a false positive (suspected thymic hyperplasia without neurologic symptoms). The remaining 108 patients were classified as true positives, resulting in a calculated PPV of 99%. CONCLUSIONS: We found that anti-AChR/MuSK fixed CBA had excellent PPV for MG. Anti-AChR positivity in one asymptomatic patient with suspected thymic hyperplasia was classified as a false positive result, although the possibility that it represents a true marker of thymic pathology in a patient who may later develop MG is challenging to exclude. The high PPV reported herein supports the use of anti-AChR/MuSK fixed CBA as first-line testing for suspected MG.
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 | 0.003 | 0.024 |
| 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) | 0.002 | 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".