The changing landscape of autoantibody testing in myasthenia gravis in the setting of novel drug treatments
Bibliographic record
Abstract
• Acquired MG is an autoimmune disease caused by antibodies against the AChR, and related proteins in the postsynaptic muscle membrane. • Serological testing plays a crucial role in MG diagnostic confirmation and therapeutic direction. • With advancements in MG diagnosis and management, its testing algorithms have been recently changed. Acquired myasthenia gravis (MG) is an autoimmune disease targeting the specific proteins in the postsynaptic muscle membrane. 50% of ocular and 80% of generalized MG have acetylcholine receptor antibodies (AChR Abs). 1–10% of MG patients have antibodies against muscle-specific kinase (MuSK), and 2–50 % of seronegative MG cases have antibodies against lipoprotein-receptor-related protein4 antibodies (LRP4 Abs). Serological testing is crucial for diagnosing and determining the appropriate therapeutic approach for MG patients. The radioimmunoprecipitation assay (RIPA) method is a historical standard test for detecting the AChR Abs and MuSK Abs. While it has nearly 100% specificity in the AChR Abs detection, its sensitivity is between 50––92%. The sensitivity and specificity of RIPA for detecting MuSK Abs is much lower. The fixed and live Cell-Based assays (f-CBA and L- CBA) have higher sensitivity than RIPA. With advancements in the serological diagnosis and management of MG, we now recommend a complete reflex testing algorithm on the first pretreatment sample of a suspected MG patient, starting with the binding and blocking assays for AChR Abs by RIPA and/ or f-CBA. If AChR Ab is negative, then reflex to MuSK Abs by RIPA and/ or CBAs. If AChR and MuSK Abs are negative, then use clustered L-CBA by request.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".