Exploring the Ethical Landscape of Pediatric Neuroendovascular Treatments
Bibliographic record
Abstract
The treatment of neurovascular disease in children continues to push clinical and innovative boundaries, carrying with them uniquely high stakes. Hemorrhage from, or occlusion of, a major cerebral blood vessel may result in devastating neurological injury or death. In children and young people, the stakes around a decision to offer or withhold treatment are even greater. The implications of long-term neurodisability in a young child who may live for many decades with the aftermath of brain injury must also be considered. Contemporary clinical management of children with neurovascular disorders involves a wide range of unique ethical challenges. Evolving technologies for neurovascular diagnosis and treatment will be accompanied by new and unprecedented ethical issues. In this article, we explore and discuss the key ethical principles, frameworks, and broader challenges pertinent to pediatric neurovascular disease and its management; challenges to informed consent, best interests, balancing beneficence, burden and nonmaleficence, ethics related to genotype-directed pharmacotherapy, and global health considerations are addressed. Precision child health will see an increase in volume of patients, particularly for tertiary/quaternary neurovascular units, where ethical considerations will be pushed into a new frontier, requiring a high level of ethical literacy from all team members.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| 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".