Conceptualizing Post Intensive Care Syndrome in Children—The PICS-p Framework*
Bibliographic record
Abstract
CONTEXT: Over the past several decades, advances in pediatric critical care have saved many lives. As such, contemporary care has broadened its focus to also include minimizing morbidity. Post Intensive Care Syndrome, also known as "PICS," is a group of cognitive, physical, and mental health impairments that commonly occur in patients after ICU discharge. Post Intensive Care Syndrome has been well-conceptualized in the adult population but not in children. OBJECTIVE: To develop a conceptual framework describing Post Intensive Care Syndrome in pediatrics that includes aspects of the experience that are unique to children and their families. DATA SYNTHESIS: The Post Intensive Care Syndrome in pediatrics (PICS-p) framework highlights the importance of baseline status, organ system maturation, psychosocial development, the interdependence of family, and trajectories of health recovery that can potentially impact a child's life for decades. CONCLUSION: Post Intensive Care Syndrome in pediatrics will help illuminate the phenomena of surviving childhood critical illness and guide outcomes measurement in the field. Empirical studies are now required to validate and refine this framework, and to subsequently develop a set of core outcomes for this population. With explication of Post Intensive Care Syndrome in pediatrics, the discipline of pediatric critical care will then be in a stronger position to map out recovery after pediatric critical illness and to evaluate interventions designed to mitigate risk for poor outcomes with the goal of optimizing child and family health.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".