Human Leukocyte Antigen Genotype as a Marker of Multiple Sclerosis Prognosis
Bibliographic record
Abstract
OBJECTIVE: In a previous pilot monocentric study, we investigated the relation between human leukocyte antigen (HLA) genotype and multiple sclerosis (MS) disease progression over 2 years. HLA-A*02 allele was correlated with better outcomes, whereas HLA-B*07 and HLA-B*44 were correlated with worse outcomes. The objective of this extension study was to further investigate the possible association of HLA genotype with disease status and progression in MS as measured by sensitive and complex clinical and imaging parameters. METHODS: Hundred and forty-six MS patients underwent HLA typing. Over a 4-year period of follow-up, we performed three clinical and magnetic resonance imaging (MRI) assessments per patient, which respectively included Expanded Disability Status Scale, Multiple Sclerosis Severity Scale, Timed-25-Foot-Walk, 9-Hole Peg Test, Symbol Digit Modalities Test, Brief Visual Memory Test, California Verbal Learning Test-II, and whole-brain atrophy, fluid-attenuated inversion recovery (FLAIR) lesion volume change and number of new FLAIR lesions using icobrain. We then compared the clinical and MRI outcomes between predefined HLA patient groups. RESULTS: Results of this larger study with a longer follow-up are in line with what we have previously shown. HLA-A*02 allele is associated with potentially better MS outcomes, whereas HLA-B*07, HLA-B*44, HLA-B*08, and HLA-DQB1*06 with a potential negative effect. Results for HLA-DRB1*15 are inconclusive. CONCLUSION: In the era of MS treatment abundance, HLA genotype might serve as an early biomarker for MS outcomes to inform individualized treatment decisions.
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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) | 0.002 | 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".