Kidney diseases and COVID-19 infection: causes and effect, supportive therapeutics and nutritional perspectives
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Recently, the novel coronavirus disease 2019 (COVID-19), has attracted the attention of scientists where it has a high mortality rate among older adults and individuals suffering from chronic diseases, such as chronic kidney diseases (CKD). It is important to elucidate molecular mechanisms by which COVID-19 affects the kidneys and accordingly develop proper nutritional and pharmacological strategies. Although numerous studies have recently recommended several approaches for the management of COVID-19 in CKD, its impact on patients with renal diseases remains the biggest challenge worldwide. In this paper, we review the most recent evidence regarding causality, potential nutritional supplements, therapeutic options, and management of COVID-19 infection in vulnerable individuals and patients with CKD. To date, there is no effective treatment for COVID-19-induced kidney dysfunction, and current treatments are yet limited to anti-inflammatory (e.g. ibuprofen) and anti-viral medications (e.g. Remdesivir, and Chloroquine/Hydroxychloroquine) that may increase the chance of treatment. In conclusion, the knowledge about kidney damage in COVID-19 is very limited, and this review improves our ability to introduce novel approaches for future clinical trials for this contiguous disease.
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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 | 0.000 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it