Bibliographic record
Abstract
Since the discovery in 2004 of NMO-IgG, the autoantibody associated with neuromyelitis optica (NMO),1 neurologists are increasingly relying on the NMO-IgG test to rule in or rule out NMO. Related disorders like optic neuritis (ON), transverse myelitis (TM), acute disseminated encephalomyelitis (ADEM), and longitudinally extensive TM (LETM; ≥3 spinal cord vertebral segments) can all be monophasic or multiphasic, can occur together or individually, and can occur in adults or children. Similarly, multiple sclerosis (MS) is often confused with these disorders, especially early in the disease. But little is known about the prevalence of NMO-IgG in children presenting with these disorders. In the current issue of Neurology ®, Dr. Banwell at the Hospital for Sick Children in Toronto, Canada, along with colleagues in Argentina and Montreal, and Dr. Pittock and colleagues at the Mayo Clinic in Rochester, Minnesota, aim to determine the seroprevalence of NMO-IgG in children with NMO and related disorders.2 This is the first published characterization of NMO-IgG seroprevalence in children and includes 87 patients with NMO, TM, ON, ADEM +TM, and MS from two centers, Toronto and Buenos Aires. Diagnoses were based on well-established clinical and radiologic criteria, and clinicians were blinded to the NMO-IgG status for the purpose of this study. The results …
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".