Redefining Success in the PICU: New Patient Populations Shift Targets of Care
Bibliographic record
Abstract
Over the last 3 decades, mortality rates of children admitted to PICUs in North America have declined significantly.1 By this measure alone, PICUs have been extremely successful, offering children the best possibility for survival and recovery after life-threatening trauma and illness. Yet as mortality rates have declined, the PICU patient population has become steadily more complex. A recent analysis of admissions across 54 PICUs in the United States ( n = 52 791) revealed that 53% of critically ill children had underlying chronic, complex illnesses.2 This finding is supported by a secondary analysis of a national administrative database in the United States that revealed comorbid illness among critically ill children increased from 35% in 1997 to 41% in 2006.3 The emergence of this new population of critically ill children reflects the medical and technological advances of recent decades.1 What do we mean by children with chronic, complex illness, and how do they impact the provision of critical care? This population has been defined as children with severe antecedent disorders; children with medical complexity, such as neuromuscular conditions and neurologic impairment; children with special health care needs; and children with a chronic comorbid illness, such as cardiovascular disease. What they have in common is a greater risk of PICU admission if they become acutely ill, along with extensive medical needs that continue long after the illness that brings them to the PICU is resolved. They are typically technology dependent, requiring a medical device to maintain body functions necessary to sustain life. Family members act … Address correspondence to Janet E. Rennick, RN, MScN, PhD, Department of Nursing, Room A-405, The Montreal Children’s Hospital, 2300 rue Tupper, Montreal, Quebec, Canada, H3H 1P3. E-mail: janet.rennick{at}muhc.mcgill.ca
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".