© 2000 Canadian Medical Association or its licensors Commentary
Bibliographic record
Abstract
An association between the risk of venous throm-boembolism and a hypercoagulable state has beenrecognized for some years. More recent advances in thrombosis research and laboratory medicine have pro-vided an ever-expanding list of specific laboratory anom-alies that may predispose people to venous thromboem-bolism. This article will review hypercoagulability with emphasis on laboratory risk markers and will provide some practical guidelines concerning the potential usefulness and limitations of such data in the context of patient manage-ment. An overview of procoagulant and anticoagulant reac-tions is shown in Fig. 1, and the conditions that promote a hypercoagulable state are summarized in Table 1. Hemostasis involves a concerted and complex series of reactions, integrating vascular, endothelial cell, platelet and plasma factor responses that regulate thrombus formation. In response to injury, or other prothrombotic stimuli, rapid changes in vascular endothelial cells will lead to both the release of intracellular proteins that participate in the he-mostatic process and an alteration of cell-surface proper-ties, promoting a thrombogenic environment. Platelet ad-hesion and activation and concurrent activation of the Risk factors for thromboembolism: pathophysiology and detection
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.043 | 0.021 |
| Insufficient payload (model declined to judge) | 0.167 | 0.107 |
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".