Physiologic Inhibitors of Coagulation in Patients on Chronic Hemodialysis
Bibliographic record
Abstract
Patients on hemodialysis are at increased risk for bleeding and thromboses. The intriguing balance between these risks is more complex than once thought, as endogenous clotting factors and their regulators come into contact with bioincompatible dialyzer membranes, in the setting of an extracorporeal circuit of blood flow, in the face of the uremic state. In this review, we summarize the current data on the interaction between the physiologic inhibitors of coagulation and hemodialysis. Data sources and study selection were obtained from research and review articles related to the endogenous anticoagulation pathway published in English on MEDLINE from 1972 to 2002. While protein C activity and protein S antigen concentrations are increased, there is no change in antithrombin III levels during hemodialysis in relation to predialysis levels. Plasma protein Z, which has only recently been studied in uremic subjects, is increased as well. In addition, hemodialysis leads to elevated tissue factor plasminogen inhibitor, thrombomodulin, tissue plasminogen activator, and plasminogen activator inhibitor-1 activities. The potential functional significance of these observations is discussed. Finally, as erythropoietin is commonly prescribed to uremic patients and is recognized to be prothrombotic, an appraisal of its interaction with the naturally occurring anticoagulants is presented. It is apparent that we are only beginning to realize the complexity of the interplay between this myriad of serum factors and hemodialysis. Further research is needed to shed light on this underexplored area of hemodialysis.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".