Vaccine Effectiveness Against SARS-CoV-2 Infection and Severe Outcomes in the Maintenance Dialysis Population in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Vaccination studies in the hemodialysis population have demonstrated decreased antibody response compared with healthy controls, but vaccine effectiveness for preventing SARS-CoV-2 infection and severe disease is undetermined. METHODS: We conducted a retrospective cohort study in the province of Ontario, Canada, between December 21, 2020, and June 30, 2021. Receipt of vaccine, SARS-CoV-2 infection, and related severe outcomes (hospitalization or death) were determined from provincial health administrative data. Receipt of one and two doses of vaccine were modeled in a time-varying cause-specific Cox proportional hazards model, adjusting for baseline characteristics, background community infection rates, and censoring for non-COVID death, recovered kidney function, transfer out of province, solid organ transplant, and withdrawal from dialysis. RESULTS: =2901, 26%); median time between the first and second dose was 36 days (IQR 28-51). The adjusted hazard ratio (HR) for SARS-CoV-2 infection and severe outcomes for one dose compared with unvaccinated was 0.59 (95% CI, 0.46 to 0.76) and 0.54 (95% CI, 0.37 to 0.77), respectively, and for two doses compared with unvaccinated was 0.31 (95% CI, 0.22 to 0.42) and 0.17 (95% CI, 0.1 to 0.3), respectively. There were no significant differences in vaccine effectiveness among age groups, dialysis modality, or vaccine type. CONCLUSIONS: COVID-19 vaccination is effective in the dialysis population to prevent SARS-CoV-2 infection and severe outcomes, despite concerns about suboptimal antibody responses.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".