Clinical relevance and utility of GAD65 antibodies in neurological disease: an eight year cohort study
Bibliographic record
Abstract
Neurological syndromes associated with glutamic acid decarboxylase (GAD) antibodies provide a challenge in understanding disease pathogenesis, interpreting antibody results, and deciding manage- ment. We retrospectively reviewed 277 patients with positive anti-GAD antibodies (≥ 10 IU/mL) at our centre between 2012-2020. 154 (56%) had one or more neurological disorders including 27 stiff person spectrum disorders (SPSD) (18%), 20 cerebellar ataxia (13%), 18 epilepsy (12%), 18 encephalitis (12%), 12 ‘mixed’ (8%), and 59 other neurological disorders (38%). Co-existing autoimmunity was common; 57 (33%) had diabetes and 46 autoimmune thyroid disease (30%). A wide range of GAD titres was seen, but serum GAD antibody titres were significantly higher in patients with ‘classical’ GAD antibody syndromes than other neurological disorders (p<0.0001) or diabetes only (p<0.0001). In patients with ‘classical’ GAD antibody syndromes, both specific neurological syndrome and the presence of diabetes influenced serum antibody titres; neurological syndrome also affected CSF titres. Patients with SPSD or encephalitis were most likely to receive immunotherapy and the most likely to respond to treatment. Overall, GAD antibody titres were not associated with response to treatment. This is largest GAD-antibody cohort reported and together these data provide key lessons for understanding and interpreting GAD antibody-associated syndromes in clinical practice.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".