Clinical outcomes of transverse myelitis with myelin oligodendrocyte glycoprotein antibody versus negative cases among adults in Isfahan, Iran: A comparative study
Bibliographic record
Abstract
Background: The aim of this study was to evaluate the status of anti-myelin oligodendrocyte glycoprotein (MOG) antibodies in patients with transverse myelitis (TM) and compare the clinical and imaging characteristics of MOG immunoglobulin G (IgG)-positive with negative cases. Methods: This cohort study enrolled 71 patients diagnosed with new-onset of TM who were being followed at a referral university clinic in Isfahan, Iran, from November 2016 to January 2019. Magnetic resonance imaging (MRI) images and blood samples for anti-MOG, anti-aquaporin 4 (anti-AQP4) (using the cell-based technique), and vasculitis-related antibodies were collected from patients. Outcomes were assessed by the evolution of the Expanded Disability Status Scale(EDSS) score and brain and spinal cord imaging findings within three months. All patients underwent imaging and clinical assessment during a mean period of one year as a follow-up. We compared the characteristics of clinical and radiological outcomes in MOG-IgG-positive and negative cases. Results: Of the total population studied, there were 26.8% men and 73.2% women, with a mean age of 33 ± 10 years. 12 (16.9%) patients were seropositive for MOG antibody and 17 (89.5%) were positive for anti-AQP4 antibodies. There was no significant association between anti-MOG antibody seropositivity and age, gender distribution, the presence of other autoimmune diseases, and number and interval of relapses. However, the involvement site of the spine at first imaging was significantly different between seronegative and seropositive patients. Conclusion: In patients with MOG antibody disease (MOG-AD) TM, the MRI findings suggest a preferential involvement of the cervical-thoracic section in seropositive cases which may help differentiate from non-MOG demyelination TM.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".