P.121 Leading the Way to the Future: Implementing Novel Therapeutics for Rare Pediatric Neurological Disorders
Bibliographic record
Abstract
Background: Children and Adolescents with rare neurogenetic disorders often have no known cure or disease modifying treatments. Recent advancements in treatments are offering much needed hope to these patients and families. However, these treatments are extremely costly, have complex administration requirements and have many unknown long-term risks and outcomes. Methods: In this presentation, we will discuss our experiences with the implementation process, including developing intricate care pathways, collaborating with multiple disciplines and services, supporting and advocating for our patients and families, and interacting with government agencies and pharmaceutical companies. Case studies will highlight the positive impact these treatments are making on the lives of children and adolescents with rare neurological disorders. Results: Spinal muscular atrophy and Neuronal Ceroid Lipofuscinosis Type 2 are both rare and devastating neurodegenerative conditions with significant morbidity and mortality. Health Canada and government funding agencies recently approved Nusinersen, Onasemnogene abeparvovec for the treatment of SMA and Cerliponase alfa for the treatment of CLN2, leading us to swiftly integrate these treatments into our standard of care. Conclusions: While implementing these novel therapies into clinical practice can be both challenging and rewarding, neuroscience nurses are positioned at the forefront to be leaders in this process at both organizational, national, and international levels.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.024 | 0.006 |
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".