In vivo Effects of Low Molecular Weight Heparins on Experimental Thrombosis and Bleeding
Bibliographic record
Abstract
Recent studies with heparin fractions indicate that it is possible to dissociate the antithrombotic and hemorrhagic effects of heparin and so improve its therapeutic potential. Heparin inhibits blood coagulation by 3 independent mechanisms by augmenting the effect of antithrombin III (the major effect), by augmenting the inhibitory effect of thrombin or heparin cofactor II, and by disrupting the activation of blood coagulation on the platelet surface; it has an additional effect on hemostasis through its interaction with blood platelets. Some insight into the mechanism of heparin-induced bleeding has been provided by studies with low molecular weight heparins. These heparins have reduced antithrombin activity but retain anti-Xa activity and have antithrombotic properties in animals with a reduced risk of bleeding. There is evidence that the reduction in the bleeding risk is unrelated to the anticoagulant effect of these low molecular weight heparins, but that it may be related to the observation that they inhibit platelet function less than standard heparin. The very low molecular weight heparins (molecular weight 3,000 daltons), have virtually no anti-IIa activity and are relatively weaker antithrombotic agents than low molecular weight heparins of 5,000 daltons. A minimal amount of anti-IIa activity is required for full expression of the antithrombotic activities of these low molecular weight heparins.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".