COVID-19 infection and the progression of kidney disease in British Columbia, Canada
Bibliographic record
Abstract
BACKGROUND: We investigated the long-term effect of COVID-19 on estimated glomerular filtration rate (eGFR) trajectory and the association with progression to kidney failure in patients with CKD. METHODS: Patients living with non-dialysis-dependent CKD from British Columbia, Canada infected with COVID-19 (cases) were matched 1:2 to non-COVID-19-infected controls on variables including pre-COVID-19 annual rate of eGFR decline. Patients were followed from 90 days from the date of COVID-19 diagnosis. The Cox proportional hazard model was used for the primary outcome of kidney failure, defined as a composite of eGFR reaching <15 ml/min/1.73 m2, initiation of maintenance dialysis or kidney transplantation. A linear mixed regression model was used to calculate the annual rate of change in eGFR. RESULTS: The study included 802 patients: 268 cases and 534 controls. The median age was 70 years and 54% were male. Over ≈3 years of follow-up, the risk of developing kidney failure did not differ significantly between cases and controls. The annual rate of eGFR decline was 2.05 ml/min/1.73 m2 among cases versus 1.35 ml/min/1.73 m2 among controls, representing a rate difference of 0.71 ml/min/1.73 m2 (P = .02). CONCLUSION: In patients with non-dialysis-dependent CKD who survived at least 90 days without requiring dialysis, COVID-19 was not associated with an increased long-term risk of kidney failure over 3 years but was associated with a greater annual decline in eGFR. Future research with longer follow-up is required to examine if this difference persists and leads to increased risk for kidney failure.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".