Endocrine and multiple sclerosis outcomes in patients with autoimmune thyroid events in the alemtuzumab CARE-MS studies
Bibliographic record
Abstract
Background Alemtuzumab is an effective therapy for relapsing multiple sclerosis. Autoimmune thyroid events are a common adverse event. Objective Describe endocrine and multiple sclerosis outcomes over 6 years for alemtuzumab-treated relapsing multiple sclerosis patients in the phase 3 CARE-MS I, II, and extension studies who experienced adverse thyroid events. Methods Endocrine and multiple sclerosis outcomes were evaluated over 6 years. Thyroid event cases, excluding those pre-existing or occurring after Year 6, were adjudicated retrospectively by expert endocrinologists independently of the sponsor and investigators. Results Thyroid events were reported for 378/811 (46.6%) alemtuzumab-treated patients. Following adjudication, endocrinologists reached consensus on 286 cases (75.7%). Of these, 39.5% were adjudicated to Graves’ disease, 2.5% Hashimoto's disease switching to hyperthyroidism, 15.4% Hashimoto's disease, 4.9% Graves’ disease switching to hypothyroidism, 10.1% transient thyroiditis, and 27.6% with uncertain diagnosis; inclusion of anti-thyroid antibody status reduced the number of uncertain diagnoses. Multiple sclerosis outcomes of those with and without thyroid events were similar. Conclusion Adjudicated thyroid events occurring over 6 years for alemtuzumab-treated relapsing multiple sclerosis patients were primarily autoimmune. Thyroid events were considered manageable and did not affect disease course. Thyroid autoimmunity is a common but manageable adverse event in alemtuzumab-treated relapsing multiple sclerosis patients. ClinicalTrials.gov Registration Numbers: CARE-MS I (NCT00530348); CARE-MS II (NCT00548405); CARE-MS Extension (NCT00930553)
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".