COVID-19 in patients undergoing long-term dialysis in Ontario
Bibliographic record
Abstract
BACKGROUND: Patients undergoing long-term dialysis may be at higher risk of infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and of associated disease and mortality. We aimed to describe the incidence, risk factors and outcomes for infection in these patients in Ontario, Canada. METHODS: We used linked data sets to compare disease characteristics and mortality between patients receiving long-term dialysis in Ontario who were diagnosed SARS-CoV-2 positive and those who did not acquire SARS-CoV-2 infection, between Mar. 12 and Aug. 20, 2020. We collected data on SARS-CoV-2 infection prospectively. We evaluated risk factors for infection and death using multivariable logistic regression analyses. RESULTS: During the study period, 187 (1.5%) of 12 501 patients undergoing dialysis were diagnosed with SARS-CoV-2 infection. Of those with SARS-CoV-2 infection, 117 (62.6%) were admitted to hospital and the case fatality rate was 28.3%. Significant predictors of infection included in-centre hemodialysis versus home dialysis (odds ratio [OR] 2.54, 95% confidence interval [CI] 1.59-4.05), living in a long-term care residence (OR 7.67, 95% CI 5.30-11.11), living in the Greater Toronto Area (OR 3.27, 95% CI 2.21-4.80), Black ethnicity (OR 3.05, 95% CI 1.95-4.77), Indian subcontinent ethnicity (OR 1.70, 95% CI 1.02-2.81), other non-White ethnicities (OR 2.03, 95% CI 1.38-2.97) and lower income quintiles (OR 1.82, 95% CI 1.15-2.89). INTERPRETATION: Patients undergoing long-term dialysis are at increased risk of SARS-CoV-2 infection and death from coronavirus disease 2019. Special attention should be paid to addressing risk factors for infection, and these patients should be prioritized for vaccination.
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 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 | 0.003 | 0.246 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".