Pediatric Neuromyelitis Optica Spectrum Disorder and Sjögren Syndrome: More Common Than Previously Thought?
Bibliographic record
Abstract
To the Editor: We read with interest the report by Kornitzer, et al 1 in The Journal and agree that increased awareness and a better understanding of the association between pediatric systemic autoimmunity and neuromyelitis optica spectrum disorder (NMOSD) is warranted. In their report, Kornitzer, et al refer to cases previously published by our group as unconfirmed cases of NMOSD because of a lack of NMO immunoglobulin G (IgG) results1. Here, we provide additional information regarding these cases, as well as 2 new cases to highlight the co-occurrence of childhood Sjögren syndrome (SS) and NMOSD. The Institutional Review Board approved this study. A waiver of consent/assent was obtained. Case 1 was an 11-year-old African American girl initially hospitalized for acute transverse myelitis and bilateral optic neuritis in the setting of sicca symptoms. Antinuclear antibody (ANA), SSA, and SSB antibodies were positive. Lip biopsy was consistent with SS (Table 1). Because the patient was diagnosed in 2003, 1 year prior to the discovery of the aquaporin-4 antibody2, NMO-IgG … Address correspondence to Dr. S. Gmuca, Department of Pediatric Rheumatology, The Children’s Hospital of Philadelphia, 34th Street and Civic Center Boulevard, Wood Building, Fourth Floor, Philadelphia, Pennsylvania 19104, USA. E-mail: gmucas{at}email.chop.edu
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".