P.012 Myelin water imaging in Anti-NMDA receptor autoimmune encephalitis; a pilot study
Bibliographic record
Abstract
Background: This study explored whether Myelin Water imaging could detect myelin injury in Anti-NMDA receptor autoimmune encephalitis (NMDAr-AIE), where traditional neuroimaging is often normal. Myelin Water Fraction (MWF) quantifies myelin content by distinguishing myelin sheath water from other brain water compartments. Methods: Adult participants with confirmed NMDAr-AIE diagnoses and healthy controls (HC) underwent 3T brain MRI including MWF mapping. Participants were recruited after discharge from the hospital. Mean MWF was calculated for 4 white matter regions of interest (ROI). Patient demographics, clinical assessments, treatment, and outcomes were collected. Results: Five participants with NMDAr-AIE (4F/1M, mean age 30, SD 7) and four HC (3F/1M, mean age 36, SD 6) were included. All NMDAr-AIE participants had normal or non-specific T2 hyperintensities on initial imaging and had received immunotherapy. The mean Modified Rankin Score (MRS) on discharge was 2. MWF (mean ± SD) for normal-appearing white matter, corpus callosum, corticospinal tract, and superior longitudinal fasciculus were 0.10±0.02, 0.12±0.02, 0.15±0.03, 0.12±0.02, which were very similar to HC at 0.09±0.02, 0.11±0.01, 0.15±0.02, and 0.11±0.02, respectively. Conclusions: Myelin Water imaging showed no myelin pathology in five NMDAr-AIE patients, with MWF values comparable to HC. This suggests that myelin pathways are relatively preserved post-recovery from AIE.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".