P.030 Antibody testing for autoimmune encephalitis: a multisite study examining clinical practices in a large Canadian city
Bibliographic record
Abstract
Background: Antibody panels are one diagnostic tool within the comprehensive evaluation of suspected autoimmune encephalitis (AIE). Over-reliance on antibody panels contributes to misdiagnosis and inflated healthcare costs. Methods: Inpatients or outpatients who had AIE antibody testing ordered from one of four adult hospitals in Calgary between January 2018 – January 2020 were included. Medical records of 150 individuals were reviewed, including those with positive antibodies or testing sent to Mayo Clinic, plus a random sample. Results: AIE antibody panels were sent for 469 individuals during the 2-year period; 42 were positive (9.0%) of which 10 were pathogenic. Of 150 individuals included in chart review, 27 (18.0%) met criteria for possible AIE at presentation and 16 (10.8%) met criteria for definite AIE at final diagnosis. Overall, antibody testing was ordered in both serum and CSF in 36.3% (versus 69.2% meeting possible AIE criteria); MRI brain was performed in 92.7% (possible AIE 92.6%), EEG in 78.7% (possible AIE 100.0%), and lumbar puncture in 66.7% (possible AIE 96.3%). A sizable proportion did not receive malignancy screening (overall 48.7%; possible AIE 29.6%). Conclusions: Antibody panels are overemphasized in the assessment for AIE and often performed unnecessarily, while other recommended clinical tests are not consistently completed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".