Research on Prediction Model of Severe Autoimmune Encephalitis
Bibliographic record
Abstract
Objective This study aimed to identify the factors that influencing the severity of autoimmune encephalitis and establish a predictive model for severe autoimmune encephalitis. Background Recognizing the predictors of disease severity in autoimmune encephalitis is essential for individualized treatment strategy. Design/Methods Clinical information were retrospectively collected from suspected autoimmune encephalitis patients at Beijing Tongren Hospital between February 2012 and May 2022. Patients meeting inclusion criteria were divided into development and validation cohorts based on the timing of antibody detection. Disease severity was assessed by modified Rankin scale (mRS) and the Clinical Assessment Scale for Autoimmune Encephalitis (CASE). Univariate and logistic regression were employed to identify factors impacting disease severity. A nomogram was constructed for predicting severe autoimmune encephalitis. Results A total of 207 patients with autoimmune encephalitis were included in the analysis of factors related to disease severity. A nomogram was developed to predict severity, incorporating variables such as age, psychiatric and/or behavioral abnormalities, seizures, impaired consciousness, cognitive impairment, involuntary movements, combined tumors, admission to ICU, increased intrathecal synthesis rate of IgG. In the development cohort, the area under curve (AUC) was 0.831 (95% CI: 0.762 - 0.899); in internal validation using the Bootstrap method, the AUC was 0.832 (95% CI: 0.707 - 0.902); in the external validation AUC was 0.800 (95% CI 0.645 - 0.956), the calibration curve shows well calibration effect. Conclusions This study established and validated a nomogram for predicting severe autoimmune encephalitis, demonstrating robust discrimination and calibration.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".