Steroids decrease diabetic risk in Glutamic Acid Decarboxylase-65 (GAD65) neurological autoimmunity: a retrospective cohort study
Bibliographic record
Abstract
Background It has become dogma that steroids should be avoided in patients with GAD65 neurological autoimmunity because of perceived increase in diabetes risk. Little prior data is available on the clinical and serological predictors of diabetes development. Methods 196 Mayo Clinic patients with high-titre (>20 nmol/L in serum; normal reference range ≤0.02nmol/L) GAD65 antibodies who had a HbA1c measured or medication list available at least 1 year post-initial therapy were identified (2003-2018). Results Diabetes was diagnosed in 86 patients (43.9%): assigned T1 status in 63 (32.3%) and type 2 status in 22 patients (11.3%). 43 (50.6%) diabetes cases occurred after neurological onset. If patients did not have diabetes prior to neurological onset, diabetes diagnosis occurred a median of 5 years later in 39.1% of patients. Of those with a diagnosis of type 2 diabetes, 45% required insulin (p= 0.01). Steroids reduced the risk of diagnosis of diabetes (p= 0.046). After stopping immunotherapy, the probability (0.69) of neu- rological relapse is high (p= 0.002). Conclusions Over half of diabetes cases occur after neurological onset. Steroids reduce, not increase, the risk of diabetes in GAD65 neurological disease. Consideration should be given to continuing mainte- nance treatment as risk of relapse after stopping immunotherapy is high.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".