The Impact of Coronavirus Disease 2019 Pandemic on U.S. and Canadian PICUs
Bibliographic record
Abstract
OBJECTIVES: There are limited reports of the impact of the coronavirus disease 2019 pandemic focused on U.S. and Canadian PICUs. This hypothesis-generating report aims to identify the United States and Canadian trends of coronavirus disease 2019 in PICUs. DESIGN AND SETTING: To better understand how the coronavirus disease 2019 pandemic was affecting U.S. and Canadian PICUs, an open voluntary daily data collection process of Canadian and U.S. PICUs was initiated by Virtual Pediatric Systems, LLC (Los Angeles, CA; http://www.myvps.org) in mid-March 2020. Information was made available online to all PICUs wishing to participate. A secondary data collection was performed to follow-up on patients discharged from those PICUs reporting coronavirus disease 2019 positive patients. MEASUREMENTS AND MAIN RESULTS: To date, over 180 PICUs have responded detailing 530 PICU admissions requiring over 3,467 days of PICU care with 30 deaths. The preponderance of cases was in the eastern regions. Twenty-four percent of the patients admitted to the PICUs were over 18 years old. Fourteen percent of admissions were under 2 years old. Nearly 60% of children had comorbidities at admission with the average length of stay increasing by age and by severity of comorbidity. Advanced respiratory support was necessary during 67% of the current days of care, with 69% being conventional mechanical ventilation. CONCLUSIONS: PICUs have been significantly impacted by the pandemic. They have provided care not only for children but also adults. Patients with coronavirus disease 2019 have a high frequency of comorbidities, require longer stays, more ventilatory support than usual PICU admissions. These data suggest several avenues for further exploration.
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 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 | 0.008 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".