Estimated glomerular filtration rate in post COVID-19 patients at 3–6 months and 12–18 months after infection
Bibliographic record
Abstract
Background Some degree of renal impairment is common during acute COVID-19 infection. However, it remains unclear whether this impairment is temporary or persists long term. In this study we compare kidney function (via estimated glomerular filtration rate [eGFR]) during infection, 3–6 months and 12–18 months after infection; the relationship between patient characteristics and eGFR in post COVID-19 patients; and the difference in eGFR between post COVID-19 patients and controls.Methods In total, 95 post COVID-19 patients and 94 controls were included. Post COVID-19 patients were seen 3–6 months and 12–18 months after infection for biological sample collection and questionnaire administration, with results for biological samples during acute infection sourced from medical records. Mixed model analyses were performed to study the associations between patient characteristics and eGFR and linear regression analyses to study the difference between post COVID-19 patients and controls.Results Under a complete case analysis among post COVID-19 patients (where results available at the acute phase and both follow-up points, n = 61), the eGFR was <90 mL/min/1.73 m2 in 50.8% during infection, in 68.9% at visit 1 and in 75.4% at visit 2, compared with 40.4% in the control group. The eGFR was lower among patients with a higher age, those who had been hospitalized, and those with CVD/hypertension. After adjusting for confounders, the eGFR at the 12–18 month time point was significantly lower in post COVID-19 patients than controls.Conclusions Previous COVID-19 infection was associated with a reduced eGFR up to 18 months after infection with higher age and CVD/hypertension increasing this likelihood.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".