P.014 Immunotherapy responses of patients with suspected autoimmune-associated epilepsy with negative neural antibody testing
Bibliographic record
Abstract
Background: In refractory epilepsy patients with possible autoimmune-associated epilepsy (AAE) but negative antibody testing(-AB), immunotherapy trials (IMT) may still be pursued.The value of (IMT) in such patients remains unclear. For this reason, we reviewed their immunotherapy responses. Methods: Retrospective review of epilepsy patients admitted to the Epilepsy Unit between (2018-2021) who received (IMT). All had (-AB) and received immunotherapy (methylprednisolone (IVMP)-immune globulin (IVIg)-plasma exchange (PLEX)- rituximab).We considered responders when their seizure reduction was ≥ 50%. Results: 14 patients identified. Of them, 50%(n=7) females. Median age (43.5 year. IQR= 28.75-63.25). All refractory to ≥ 2 anti-seizure medications (ASM). Median epilepsy onset was (39.5 years. IQR=23.75-60.25).Median time from diagnosis until received immunotherapy was (15.5 months. IQR=12.75 -21.75). Patients received either IVIG+IVMP (35.7%, n=5) or IVIG alone (28.5%, n=4) or IVIG+IVMP+PLEX (21.4%, n=3) or IVMP alone (7.1%, n=1) or IVIG+IVMP+rituximab (7.1%, n=1). Median follow-up was 25 months.Although early immunotherapy responses were common, sustained response to immunotherapy at last follow-up was only in 21.4% (n=3). Factors confounding determination of immunotherapy efficacy were present in all responders (e.g:concurrent changes in ASM). Conclusions: Our findings suggest that (IMT) in patients with suspected (AAE) but with (-AB) are largely unsuccessful. This suggests an insufficient therapeutic effect after (IMT) or alternatively,non-immune-mediated mechanisms causing this type of epilepsy. Critical evaluations of (IMT)in such cases are needed.
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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 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".