COVID-19 screening and outcomes at hospitals in a large Canadian health authority
Bibliographic record
Abstract
Background: This study investigates factors associated with COVID-19 positivity among patients admitted to hospitals in British Columbia, Canada, and analyzes patient outcomes based on their screening question responses. Methods: We conducted a retrospective analysis of patients admitted to 12 hospital emergency departments between November 1, 2020, and June 30, 2022. Patients who tested positive for SARS-CoV-2 through PCR within 48 hours of admission were categorized as positive cases. Covariates included age, geographical region, and the era of the COVID-19 pandemic. Results: Among the 88,511 unique admissions, 8.6% (7,642) tested positive for COVID-19. Patients who met screening criteria were 4.7 times more likely to test positive. Patients in the later stages of the pandemic were less likely to be identified through screening questions. Patients who tested positive were 1.5 times more likely to die than those who tested negative, although patients who tested positive in later pandemic stages had lower overall mortality rates. Conclusion: While patients testing positive on admission were more likely to meet screening criteria and had a higher risk of mortality, the screening process missed half of all positive cases (3,907 patients). Implementing universal testing increased resource demands but identified the positive cases missed by screening alone, allowing for the implementation of precautionary measures to prevent potential transmission. Ultimately, the decision to implement universal testing should consider resource allocation, community prevalence, and patient demographics.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".