Outcomes of hospital-acquired SARS-CoV-2 infection in the Canadian first wave epicentre: a retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: During the first wave of the COVID-19 pandemic, a substantial number of Quebec hospitals were hit by hospital-acquired (HA) SARS-CoV-2 infections. Our objective was to assess whether mortality is higher in HA cases than in non-hospital-acquired (NHA) cases and determine the prevalence of HA-SARS-CoV-2 infection in our hospital. METHODS: This retrospective single-centre cohort study included all adults (≥ 18 yr) who had COVID-19, admitted to Hôpital Maisonneuve-Rosemont (Montréal, Canada) from Mar. 1 to June 30, 2020. We collected data on demographic characteristics, comorbidities, treatment, admission to the intensive care unit (ICU) and mechanical ventilation requirements from electronic health records. We adjudicated hospital acquisition based on the timing of symptom onset, and polymerase chain reaction testing for and exposures to SARS-CoV-2. To evaluate the association between HA-SARS-CoV-2 infection and in-hospital mortality, we computed a multivariable logistic regression analysis including known risk factors for death in patients with COVID-19 as covariates. RESULTS: = 0.001) were lower. Multivariable logistic regression analysis showed that HA-SARS-CoV-2 infection in patients younger than 75 years is an independent risk factor for death (odds ratio 2.78, 95% confidence interval 1.44-5.38). INTERPRETATION: Our results show that HA-SARS-CoV-2 infection in younger patients was associated with higher mortality. Future studies need to evaluate relevant patient-centred long-term outcomes in this population.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".