Testing for <i>N</i>-methyl-<scp>d</scp>-aspartate Receptor Autoantibodies in Clinical Practice
Bibliographic record
Abstract
BACKGROUND: The diagnosis of anti-N-methyl-d-aspartate receptor (NMDAR) encephalitis relies on the detection of NMDAR IgG autoantibodies in the serum or cerebrospinal fluid (CSF) of symptomatic patients. Commercial kits are available that allow NMDAR IgG autoantibodies to be measured in local laboratories. However, the performance of these tests outside of reference laboratories is unknown. OBJECTIVES: To report an unexpectedly low rate of NMDAR autoantibody detection in serum from patients with anti-NMDAR encephalitis tested using a commercially available diagnostic kit in an exemplar clinical laboratory. METHODS: Paired CSF and serum samples from seven patients with definite anti-NMDAR encephalitis were tested for NMDAR IgG autoantibodies using commercially available cell-based assays run according to manufacturer's recommendations. Rates of autoantibody detection in serum tested at our center were compared with those derived from systematic review and meta-analyses incorporating studies published during or before March 2019. RESULTS: NMDAR IgG autoantibodies were detected in the CSF of all patients tested at our clinical laboratory but not in paired serum samples. Rates of the detection were lower than those previously reported. A similar association was recognized through meta-analyses, with lower odds of NMDAR IgG autoantibody detection associated with serum testing performed in nonreference laboratories. CONCLUSIONS: Commercial kits may yield lower-than-expected rates of NMDAR IgG autoantibody detection in serum when run in exemplar clinical (nonreference) laboratories. Additional studies are needed to decipher the factors that contribute to lower-than-expected rates of serum positivity. CSF testing is recommended in patients with suspected anti-NMDAR encephalitis.
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 | 0.024 | 0.118 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".