Pediatric neurocritical care in the 21st century
Bibliographic record
Abstract
PURPOSE OF REVIEW: Approximately one in five children admitted to a pediatric ICU have a new central nervous system injury or a neurological complication of their critical illness. The spectrum of neurologic insults in children is diverse and clinical practice is largely empirical, as few randomized, controlled trials have been reported. This lack of data poses a substantial challenge to the practice of pediatric neurocritical care (PNCC). PNCC has emerged as a novel subspecialty, and its presence is expanding within tertiary care centers. This review highlights the recent advances in the field, with a focus on traumatic brain injury (TBI), cardiac arrest, and stroke as disease models. RECENT FINDINGS: Variable approaches to the structure of a PNCC service have been reported, comprising multidisciplinary teams from neurology, critical care, neurosurgery, neuroradiology, and anesthesia. Neurologic morbidity is substantial in critically ill children and the increased use of continuous electroencephalography monitoring has highlighted this burden. Therapeutic hypothermia has not proven effective for treatment of children with severe TBI or out-of-hospital cardiac arrest. However, results of studies of severe TBI suggest that multidisciplinary care in the ICU and adherence to guidelines for care can reduce mortality and improve outcome. SUMMARY: There is an unmet need for clinicians with expertise in the practice of brain-directed critical care for children. Although much of the practice of PNCC may remain empiric, a focus on the regionalization of care, creating defined training paths, practice within multidisciplinary teams, protocol-directed care, and improved measures of long-term outcome to quantify the impact of such care can provide evidence to direct the maturation of this field.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".