P.072 The spectrotemporal characteristics of NMDA receptor encephalitis
Bibliographic record
Abstract
Background: NMDA receptor encephalitis (NMDA-RE) is an autoimmune disorder caused by antibodies to the NR1-NR2B heterodimer of the NMDA receptor. Currently, disease status is tracked primarily by the presence of auto-antibodies in the cerebrospinal fluid (CSF) and serum. Using serological and CSF markers along with clinical parameters to track disease progress can be challenging since patient symptoms and disease progress can vary widely. Methods: EEGs were reviewed in a 31 year old male patient with proven NMDA-RE. EEG data were sampled from various times before and after diagnosis, as well as during various stages of treatment. All analyses were performed using Matlab (Mathworks). Results: We showed that using a simple 1/f model of spectral behaviour (Buzsaki and Draguhn, 2004), we could fit the power spectra of the raw data at various instances during routine EEGs. We have demonstrated that the values of specific fitting parameters vary in relationship to the patient’s clinical status across various stages of illness. Conclusions: The aim of this project was to explore the potential utility of EEG as a complement to the usual clinical metrics used in monitoring NMDA-RE. The analysis techniques presented here highlight the use of EEG as a practical, minimaly-invasive tool to monitor progress and potentially aid in clinical decision making.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".