79 Impact of COVID-19 wave 3 paediatric inpatient unit closures on transfers to tertiary care paediatric hospital
Bibliographic record
Abstract
Abstract Background During Wave 3 of the COVID-19 pandemic, 15 community hospital paediatric inpatient units (comprising 167 beds) in Toronto were directed to close by the Greater Toronto Area (GTA) Hospital Incident Management System (IMS) Command Centre to increase adult inpatient bed capacity. All paediatric patients from closed inpatient units were redirected to a single tertiary care paediatric hospital, which increased capacity to accommodate these additional patients through activation of surge plans, while community hospitals redeployed resources to fill much needed gaps in adult care. Objectives The objective was to describe patient characteristics of all transfers during the closure to explore the impact of community paediatric inpatient unit closures on transfers to the tertiary hospital. Design/Methods A chart review of all transferred patients was conducted during the mandated closure and subsequent reopening. Transfers excluded ICU-level transfers as these were not impacted by IMS mandated closures. All transfers were categorized as requiring tertiary care (i.e. would typically be transferred) or not requiring tertiary care (i.e. only transferred due to the closure). Variables collected included sending hospital, admitting diagnosis, patient age, hospital disposition, and length of stay. Data was collected until the last paediatric unit reopened. Quality improvement project approval was granted by the institution. Results A total of 858 patients were transferred to the tertiary hospital during the 67 day closure; of those, 530 were transferred solely to increase adult bed capacity (i.e. were categorized as patients requiring non-tertiary care). The majority of patients were admitted to general paediatrics (52%), and 39% went to a surgical inpatient unit. Most patients (68%) admitted had a length of stay between 24 and 72 hours. A third of patients admitted were under 2 years old, and a third were over 12 years old. The top three diagnoses for admission were infections, gastrointestinal issues, and general surgery. Two-thirds (60%) of transfers from closed sites came from three sites. Conclusion More than half of the transfers occurred solely due to the mandated closures, and transfers returned to a stable volume once all sites re-opened. The GTA hospital system was able to respond to the mandated closure effectively through clear high-level communication, escalation processes and structures as well as responsive, real-time problem solving. Closures increased potential adult inpatient capacity by 6740 bed days and demonstrated an unprecedented system-wide approach to the provision of integrated paediatric care across the region.
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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".