Anticoagulation therapy in COVID-19 patients with chronic kidney disease
Bibliographic record
Abstract
Coagulopathy and derangements in the coagulation parameters are significant features of COVID-19 infection, which increases the risk of disseminated intravascular coagulation, thrombosis, and hemorrhage in these patients, resulting in increased morbidity and mortality. In times of COVID-19, special consideration should be given to patients with concurrent chronic kidney disease (CKD) and COVID-19 (CKD/COVID-19 patients) as renal dysfunction increases their risk of thrombosis and hemorrhage, and falsely affects some of the coagulation factors, which are currently utilized to assess thrombosis risk in patients with COVID-19. Hence, we believe extra attention should be given to determining the risk of thrombosis and bleeding and optimizing the timing and dosage of anticoagulant therapy in this unique population of patients. CKD/COVID-19 patients are considered a high-risk population for thrombotic events and hemorrhage. Furthermore, effects of renal function on paraclinical and clinical data should be considered during the evaluation and interpretation of thrombosis risk stratification. Individualized evaluation of clinical status and kidney function is necessary to determine the best approach and management for anticoagulant therapy, whereas there is a lack of studies about the population of CKD/COVID-19 patients who need anticoagulant therapy now.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".