Bibliographic record
Abstract
BACKGROUND: Although the pathophysiological mechanisms remain elusive, accumulating experimental and clinical data are showing that anticoagulants, particularly low molecular weight heparin, may have an important role as anticancer agents. Although this concept was first introduced decades ago, advancement in research has been hampered by scepticism and disinterest. The difficulty with understanding and defining the mechanisms of action is reflective of the diverse activity and pharmacological profile of these biological compounds, and the limitations of experimental techniques available to explore the interactions between the coagulation cascade and intracellular pathways that govern cell growth and differentiation. OBJECTIVES: This review will address and summarize some of the ongoing basic and clinical research on heparin as an anticancer therapeutic. METHODS: A literature review using the keys words 'heparin', 'low molecular weight heparin', 'cancer survival' and 'neoplasm' was performed. Meeting proceedings from recent conferences on thrombosis and cancer were handsearched for relevant clinical studies. CONCLUSION: The investigation of anticoagulants as anticancer agents is now an innovative and rapidly growing field. Greater understanding of the interaction between coagulation and cancer will lead to improved patient care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.003 | 0.002 |
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".