What happened to the patients? Care trajectories for persons with a delayed hospital discharge during wave 1 of COVID-19 in Ontario, Canada; a population-based retrospective cohort study
Bibliographic record
Abstract
During the initial wave of coronavirus disease of 2019 (COVID-19), patients were rapidly discharged from acute hospitals in anticipation of an expected influx of patients with COVID-19. Patients that were no longer receiving acute medical care but were waiting for their next destination (i.e., delayed hospital discharge) were particularly affected. The objectives of this study were to examine the impact of COVID-19 onset on healthcare utilization and mortality among those who experienced delayed discharge from acute care. We conducted a population-based retrospective cohort study using linked administrative data. We included persons discharged from acute care who experienced a delayed hospital stay between April 1, 2019 and September 30, 2020. The onset of COVID-19 was the exposure (March 1, 2020), while the period of April 1, 2019 to February 29, 2020 was considered as a comparator. Primary outcomes included healthcare utilization and mortality following discharge, stratified by care setting (homecare, inpatient rehabilitation or long-term care). Multivariable logistic, zero-inflated Poisson regressions, and Cox proportional hazard models were used to examine the impact of COVID-19 on outcomes while adjusting for covariates. Those discharged home were more likely to receive homecare and physician visits within 30 days during COVID-19. The type of visits examined included both in-person as well as virtual visits. Individuals discharged to inpatient rehabilitation experienced lower rates of general physician visits but higher rates of specialist and homecare visits. Patients discharged to long-term care were significantly less likely to receive a physician visit following COVID-19, and significantly more likely to be readmitted within 7-days. There were no significant differences in mortality irrespective of discharge destination during the two time periods. Overall, the onset of the initial wave of COVID-19 significantly impacted healthcare utilization among those with a delayed discharge but varied depending on destination, with those in long-term care being most impacted.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".