The relationship between HLA-DRB1 alleles and optic neuritis in Irish patients and the risk of developing multiple sclerosis
Bibliographic record
Abstract
AIMS: To investigate the role of the major histocompatibility complex in Irish patients with optic neuritis (ON) and determine whether HLA-DRB1 genotypes are a risk factor for the development of multiple sclerosis (MS) in such patients. METHOD: All patients were Caucasian, had Irish ancestry and had MRI of brain and optic nerves within 2-3 weeks of presentation. Patients were referred to a neurologist if MRI findings were consistent with a diagnosis of MS. HLA-DRB1 allele and phenotype frequencies for 78 patients with a clinical diagnosis of acute ON were compared with those for 250 healthy bone marrow donors. RESULTS: An ON/MS positive patient was 3.4 times more likely than an ON/MS negative patient to be DRB1*15 positive. No difference in age profile was detected between ON/MS positive and ON/MS negative patients or between the ON male and female subgroups. No gender or HLA-DRB1 association was identified for ON/MS negative patients. Female gender was significantly increased among ON/MS positive patients with a p value of 0.0053. CONCLUSIONS: DRB1*15 is a significant predisposing factor for ON. This ON patient cohort has also provided an opportunity to evaluate the relationship of HLA genotype with the risk of MS development. The findings of this study indicate that Irish individuals presenting with ON and who are HLA DRB1*15 positive have a higher risk than HLA DRB1*15 negative patients of presenting with MRI findings indicative of MS. This study has also demonstrated that female gender is a risk factor for developing MS in the Irish population.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".