Patients in hospital with laboratory-confirmed COVID-19 in a network of Canadian acute care hospitals, Mar. 1 to Aug. 31, 2020: a descriptive analysis
Bibliographic record
Abstract
BACKGROUND: Information on the epidemiology of patients in hospital with laboratory-confirmed coronavirus disease 2019 (COVID-19) in Canadian acute care hospitals is needed to inform infection prevention and control strategies and public health measures. The aim of this surveillance was to describe the epidemiology of patients in hospital with laboratory-confirmed COVID-19 in a network of Canadian acute care hospitals between Mar. 1 and Aug. 31, 2020. METHODS: Through prospective surveillance, we identified adult and pediatric patients in hospital with laboratory-confirmed COVID-19 using a standard definition between Mar. 1 and Aug. 31, 2020, through the Canadian Nosocomial Infection Surveillance Program (CNISP), a network of 78 hospitals. Patient demographic and clinical characteristics and data on treatment, interventions and outcomes were reviewed and described. RESULTS: = 37, 1.9%). Acquisition of COVID-19 in hospitals was infrequent (6.4% of all cases). A total of 32.8% of patients were admitted from a long-term care facility or retirement home. Health care workers constituted 10.6% of adult patients aged 18-65 years in hospital with COVID-19. Thirty-day attributable mortality was 16.2%. Hospital admission rates peaked in mid-April and were highest in Ontario and Quebec. INTERPRETATION: Surveillance findings indicate that a high proportion of Canadian patients in hospital with COVID-19 during the first 6 months of the pandemic were older adults with underlying medical conditions. Active surveillance of patients in hospital with COVID-19 is critical to enhancing our knowledge of the epidemiology of COVID-19 and to identifying populations at risk for severe outcomes, which will help guide Canada's response in the coming months.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".