Post COVID-19 condition imposes significant burden in patients with advanced chronic kidney disease: A nested case-control study
Bibliographic record
Abstract
BACKGROUND: The burden of post COVID-19 condition (PCC) is not well studied in patients with advanced kidney disease. METHODS: A large prospective cohort of SARS-CoV-2 vaccinated patients with chronic kidney disease stages G4-G5 (CKD G4/5), on dialysis, and kidney transplant recipients (KTR) were included. Antibody levels were determined after vaccination. Presence of long-lasting symptoms was assessed in patients with and without prior COVID-19 and compared using logistic regression. In patients with prior COVID-19, PCC was defined according to the WHO definition. RESULTS: Two hundred sixteen CKD G4/5 patients, 375 dialysis patients, and 2005 KTR were included. Long-lasting symptoms were reported in 204/853 (24%) patients with prior COVID-19 and in 297/1743 (17%) patients without prior COVID-19 (aOR: 1.45 (1.17-1.78)], P < 0.001). PCC was prevalent in 29% of CKD G4/5 patients, 21% of dialysis patients, and 24% of KTR. In addition, 69% of patients with PCC reported (very) high symptom burden. Odds of PCC was lower per 10-fold increase in antibody level after vaccination (aOR 0.82 [0.70-0.96], P = 0.01) and higher in case of COVID-19 related hospital admission (aOR 4.64 [2.61-8.25], P = 0.003). CONCLUSIONS: CKD G4/5 patients, dialysis patients, and KTR are at risk for PCC with high symptom burden after SARS-CoV-2 vaccination, especially if antibody levels are low and in case of hospitalization due to COVID-19.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| 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".