Pragmatic Recommendations for the Prevention and Treatment of Acute Kidney Injury in Patients with COVID-19 in Low- and Middle-Income Countries
Bibliographic record
Abstract
Current recommendations for the management of patients with COVID-19 and acute kidney injury (AKI) are largely based on evidence from resource-rich settings, mostly located in high-income countries. It is often unpractical to apply these recommendations to resource-restricted settings. We report on a set of pragmatic recommendations for the prevention, diagnosis, and management of patients with COVID-19 and AKI in low- and middle-income countries (LMICs). For the prevention of AKI among patients with COVID-19 in LMICs, we recommend using isotonic crystalloid solutions for expansion of intravascular volume, avoiding nephrotoxic medications, and using a conservative fluid management strategy in patients with respiratory failure. For the diagnosis of AKI, we suggest that any patient with COVID-19 presenting with an elevated serum creatinine level without available historical values be considered as having AKI. If serum creatinine testing is not available, we suggest that patients with proteinuria should be considered to have possible AKI. We suggest expansion of the use of point-of-care serum creatinine and salivary urea nitrogen testing in community health settings, as funding and availability allow. For the management of patients with AKI and COVID-19 in LMICS, we recommend judicious use of intravenous fluid resuscitation. For patients requiring dialysis who do not have acute respiratory distress syndrome (ARDS), we suggest using peritoneal dialysis (PD) as first choice, where available and feasible. For patients requiring dialysis who do have ARDS, we suggest using hemodialysis, where available and feasible, to optimize fluid removal. We suggest using locally produced PD solutions when commercially produced solutions are unavailable or unaffordable.
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 | 0.018 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".