Bleeding and venous thromboembolism in the critically ill with emphasis on patients with renal insufficiency
Bibliographic record
Abstract
PURPOSE OF REVIEW: The purpose of this review is to critique and summarize clinical literature relevant to thromboprophylaxis in critically ill patients with renal insufficiency. The specific objectives are to discuss factors that increase the risks for bleeding and venous thromboembolism in critically ill patients, with a focus on patients with renal insufficiency, and to consider prophylaxis management options and the rationale for their use. RECENT FINDINGS: Herein, we discuss both bleeding and venous thromboembolism in this population, both of which are of concern as complications. Bleeding is common among critically ill patients and has important clinical consequences. Critically ill patients with renal insufficiency require special consideration in regard to thromboprophylaxis. Such patients have a four-fold higher risk for developing venous thromboembolism compared with ICU patients without renal insufficiency. ICU patients have dynamic risks of thrombosis and bleeding. Invasive procedures may require temporary interruption of anticoagulants. Consequently, approaches to thromboprophylaxis require daily reevaluation. SUMMARY: We provide some considerations for practice in the conclusion section.
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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) | 0.003 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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 | 0.001 |
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".