P.122 The Pediatric Neuroirritability Management Protocol at the Stollery Children’s Hospital – Inspired by an Irritable Infant with GM3 Synthase Deficiency
Bibliographic record
Abstract
Background: We describe an infant with a diagnosis of GM3 synthase deficiency, presenting with severe neuroirritability from birth. He required multiple admissions due to extreme agitation and caregiver burnout. Multiple pharmacological agents were tried, and the effect of each medication was modest and short-lasting at best. The literature on the management of neuroirritability in children with progressive genetic and metabolic conditions is sparse, and a neuroirritability management protocol has yet to be developed at our institution. Methods: We searched for relevant primary research and articles on PubMed. We reviewed the evidence of each pharmacological agent and added non-pharmacological strategies. We developed management guidelines for neuroirritability at our hospital. This protocol was reviewed by several pediatric neurologists and pediatric palliative care specialists at the Stollery and SickKids Hospitals. Results: We present the Pediatric Neuroirritability Management Protocol for the Stollery Children’s Hospital. Conclusions: Further study is required to assess whether this protocol can be adapted to treat irritability in the context of other neurological conditions such as hypoxic-ischemic encephalopathy and non-accidental injury. In addition, we will expand our guidelines to include other symptoms such as spasticity, dystonia, and autonomic dysfunction.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 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".