Autoantibodies to Low-Density Lipoprotein Receptor-Related Protein 4 in Double Seronegative Myasthenia Gravis: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Myasthenia gravis (MG) is an autoimmune disorder of the neuromuscular junction in which a clinical diagnosis may be confirmed with serological testing. The most common autoantibodies used to support a diagnosis of MG are anti-acetylcholine receptor antibodies and anti-muscle-specific tyrosine kinase antibodies. In cases in which both of these autoantibodies are negative (termed double-seronegative [dSNMG]), other autoantibodies such as low-density lipoprotein receptor-related protein 4 (LRP4) may be used to aid in diagnosis. METHODS: We have undertaken a systematic literature review to identify studies that have assessed the frequency of anti-LRP4 antibodies in dSNMG patients and the characteristics of anti-LRP4+ dSNMG patients (epidemiology, clinical features, electromyographic findings, or management). PubMed, EMBASE, Medline, and Scopus were searched on January 14, 2017, using the medical subject headings "myasthenia gravis" and "low-density lipoprotein receptor-related protein 4" or "LRP4." RESULTS: The initial search identified 367 articles. Fourteen publications met the inclusion criteria. There were ten cross-sectional research studies, three were case series, and one was a case report. The majority of studies were limited by small sample sizes of LRP4+ dSNMG. There has been a wide range of frequencies of anti-LRP4 antibodies detected in different MG patient populations, some involving different laboratory techniques. CONCLUSIONS: LRP4+ dSNMG is more likely than LRP4- dSNMG to have a younger onset of disease and occur in females. LRP4+ dSNMG most often is mild in severity and often involves isolated ocular weakness. It typically responds well to pyridostigmine or prednisone.
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 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 | 0.011 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| 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; both teacher heads agree on what is shown here.
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".