Rituximab for Pediatric Central Nervous System Inflammatory Disorders in Alberta, Canada
Bibliographic record
Abstract
ABSTRACT: Background: Early and effective treatment of central nervous system (CNS) inflammatory disorders is vital to reduce neurologic morbidity and improve long-term outcomes in affected children. Rituximab is a B-cell-depleting monoclonal antibody whose off-label use for these disorders is funded in the province of Alberta, Canada, by the Short-Term Exceptional Drug Therapy (STEDT) program. This study describes the use of rituximab for pediatric CNS inflammatory disorders in Alberta. Methods: Rituximab applications for CNS inflammatory indications in patients <18 years of age were identified from the STEDT database between January 1, 2012, and December 31, 2019. Patient information was linked to other provincial datasets including the Discharge Abstract Database, Pharmaceutical Information Network, and Provincial Laboratory data. Analysis was descriptive. Results: Fifty-one unique rituximab applications were identified, of which 50 were approved. New applications increased from one in 2012 to a high of 12 in 2018. The most common indication was autoimmune encephalitis without a specified antibody (n = 16, 31%). Most children were approved for a two-dose (n = 33, 66%) or four-dose (n = 16, 32%) induction regimen. Physician-reported outcomes were available for 24 patients, of whom 14 (58%) were felt to have fully met outcome targets. Conclusion: The use of rituximab for pediatric CNS inflammatory disorders has increased, particularly for the indication of autoimmune encephalitis. This study identified significant heterogeneity in dosing practices and laboratory monitoring. Standardized protocols for the use of rituximab in these disorders and more robust outcome reporting will help better define the safety and efficacy of rituximab in this 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".