Admission Criteria and Successful Care of Adults in a Tertiary Care Pediatric Hospital During the COVID-19 Pandemic
Bibliographic record
Abstract
Objective: The purpose of this study was to review the health outcomes of adult patients selected using admission criteria and subsequently admitted on pediatric wards and intensive care unit (PICU) of the Montreal Children’s Hospital (MCH) in Canada during the first and second waves of the COVID-19 pandemic. Methods: A retrospective chart review was conducted and included all adult patients hospitalized at the MCH on pediatric units between March 14, 2020 and March 31, 2021. Results : Forty adults were admitted to MCH pediatric units. The median age was 28.5 years. There were 26 females and 14 were males. The average length of stay (LOS) at the MCH was 6.3 days. There were 32 consultations from adult medical consultants performed at the MCH. Seventeen incident reports were completed. There were no in-hospital deaths and 6 in-hospital incidents were reported. Of the 24 patients that were discharged home from the MCH units, 1 patient (4%) returned, and an additional 4 patients (16.6%) returned within 30 days. Conclusion : With clear admission criteria, careful planning, and well-planned supportive resources treating adult patients within a pediatric care facility by pediatric care teams also caring for pediatric patients in the same units could be considered as a safe contingency plan in a time of crisis.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".