The care of critically ill adults with COVID-19 in Ontario pediatric intensive care units
Bibliographic record
Abstract
PURPOSE: To describe and review the experience of two pediatric intensive care units (PICUs) in Ontario, Canada, adapting and providing care to critically ill adults during the COVID-19 pandemic. CLINICAL FEATURES: At a time of extreme pressure to adult intensive care unit (ICU) capacity, two PICUs provided care to critically ill adults with COVID-19 pneumonia. Substantial yet rapid planning was required to facilitate safe delivery of critical care to adult patients while maintaining PICU services, including thoughtful development of care pathways and patient selection. To prepare clinical staff, several communication strategies, knowledge translation, skill consolidation, and system-adaptation mechanisms were developed. There was iterative adaptation of operational processes, including staffing models, specialist consultation, and the pharmacy. Care provided by the interprofessional teams was reoriented as appropriate to the needs of critically ill adults in close collaboration with adult ICU teams. Forty-one adults were admitted to the two PICUs over a 12-week period. In total, 36 patients (88%) received invasive ventilation, eight patients (20%) were supported with venovenous extracorporeal membrane oxygenation, and six patients (15%) received continuous renal replacement therapy. Four died in the PICU during this period. Feedback from staff included anxiety around reorienting practice to the care of critically ill adults, physical exhaustion, and psychological distress. Importantly, staff also reported a renewed sense of purpose with participation in the program. CONCLUSION: Though challenging, the experience has provided opportunity to enhance collaboration with partner institutions and improve the care of older children and adolescents in the PICU.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".