A Clinical Response Team Providing Support to Long-Term Care Homes with COVID-19 Outbreaks in Eastern Ontario—a Cohort Study
Bibliographic record
Abstract
BackgroundThe greatest impact of the COVID-19 pandemic in Canada has been on long-term care facilities which have accounted for a large majority of the mortality seen in this country. We developed a clinical response team to perform mass as-sessment and provide support to long-term care facilities in Eastern Ontario with large outbreaks in the hope of reducing the impact of the outbreaks. MethodsThis is a retrospective cohort study of all residents of LTC facilities supported by our multidisciplinary clinical response team. We collected data about the timing of the outbreak and our deployment, as well as the total number of COVID-19 cases and deaths, and measured the correlation between the timing of our deployment and the observed mortality rate. ResultsOur clinical team was deployed to 14 long-term care facilities, representing 719 cases and 243 deaths (mean ± standard error of mortality 34% ± 4%). Our team was deployed a mean ± standard error of 16 ± 2 days after the declaration of an out-break. There was a significant correlation between an earlier deployment of our clinical team and a lower mortality rate for that outbreak (Pearson’s r = 0.70, p < .01). InterpretationThis retrospective, uncontrolled study of a non-standardized intervention has many potential limitations. However, the data suggest that timely deployment of our clinical response team may improve outcomes in the event of a large outbreak. This clinical team may be useful in future pandemics.
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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 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".