Recommendations from the International Paediatric Stroke Organization on pediatric neurointerventional best practices based on Delphi consensus
Bibliographic record
Abstract
Paediatric neurointervention (PNI) markedly differs from adult neuro-intervention, requiring highly subspecialized clinical skills, knowledge, and techniques. Minimum standards of practice are well established in adult neurointervention but are lacking in the field of PNI. We sought to develop expert consensus on best practices for neurointervention in children.Using a two-stage Delphi consensus model we sought expert opinions from PNI practitioners worldwide regarding best practices. A two-stage online de-identified survey of PNI practitioners was undertaken assessing opinions on a range of topics including minimum recommended caseloads for PNI centres. Minimum agreement rates of >60% were set to determine consensus on any specific question. Consensus opinions on best practices were reviewed by the International Paediatric Stroke Organization Executive Committee.For the first-stage survey there were n=50 responses and for the second-stage n=45 responses, with practitioners from all inhabited continents represented. Consensus-based best practices included: i) Elective endovascular therapeutic neuro-interventions should be performed in high-volume paediatric centres with an established multi-disciplinary paediatric neurovascular team, and ii) High-volume centres are those that undertake at least 20 paediatric endovascular therapeutic neuro-interventions annually. Paediatric thrombectomy in large-vessel occlusion stroke, an area of increasing interest and attention, poses unique time-sensitive multidisciplinary logistical challenges meriting a dedicated analysis, and as such is not within the purview of this report.Best practices for PNI reported here have been identified through expert consensus and are designed to enhance patient safety whilst providing appropriate clinical access to life-saving procedures.
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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 | 0.270 | 0.272 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.007 | 0.016 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".