Redefining Success in the PICU: New Patient Populations Shift Targets of Care
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it