Long-Term Outcomes and the Post-Intensive Care Syndrome in Critically Ill Children: A North American Perspective
Bibliographic record
Abstract
Advances in medical and surgical care for children in the pediatric intensive care unit (PICU) have led to vast reductions in mortality, but survivors often leave with newly acquired or worsened morbidity. Emerging evidence reveals that survivors of pediatric critical illness may experience a constellation of physical, emotional, cognitive, and social impairments, collectively known as the "post-intensive care syndrome in pediatrics" (PICs-P). The spectrum of PICs-P manifestations within each domain are heterogeneous. This is attributed to the wide age and developmental diversity of children admitted to PICUs and the high prevalence of chronic complex conditions. PICs-P recovery follows variable trajectories based on numerous patient, family, and environmental factors. Those who improve tend to do so within less than a year of discharge. A small proportion, however, may actually worsen over time. There are many gaps in our current understanding of PICs-P. A unified approach to screening, preventing, and treating PICs-P-related morbidity has been hindered by disparate research methodology. Initiatives are underway to harmonize clinical and research priorities, validate new and existing epidemiologic and patient-specific tools for the prediction or monitoring of outcomes, and define research priorities for investigators interested in long-term outcomes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".