La thérapie ciblée en oncologie et la pointe de l'iceberg Deuxième partie : L'angiogenèse
Bibliographic record
Abstract
Resume Objectif : Discuter de l’angiogenese, du facteur de croissance de l’endothelium vasculaire et de ses inhibiteurs dans le traitement de certains cancers. Discuter des medicaments commercialises au Canada et qui ciblent ces recepteurs. Source des donnees : Une recherche faite dans Medline, couvrant la periode s’etendant jusqu’en decembre 2007. Selection des etudes et extraction des donnees : Nous avons retenu les etudes de phase III sur les indications reconnues ou acceptees et publiees en entier ou sous forme d’abreges. Analyse des donnees : Deux classes de medicaments sont utilisees presentement pour bloquer l’activation de ces recepteurs : les anticorps monoclonaux, qui agissent sur la partie externe du recepteur cellulaire, et les inhibiteurs de la tyrosine kinase, qui agissent dans la zone intracellulaire. Conclusion : Les etudes marquantes sur le bevacizumab, le sunitinib et le sorafenib montrent que ces medicaments pourraient jouer un role dans la lutte contre la croissance d’un cancer. Cependant, il faudra determiner comment ils peuvent etre utilises de facon optimale avec ou sans chimiotherapie conventionnelle. Abstract Objective: To discuss angiogenesis and various inhibitors of vascular endothelial growth factor used in the treatment of certain cancers. To discuss drugs available in Canada that target these receptors. Data source: A Medline search was done, covering the period up to December, 2007. Study selection and data extraction: Phase III studies were retained on the basis of accepted or recognized indications, whether they were published in their entirety or under abstract form. Data analysis: Two classes of drugs are currently used to block the activation of these receptors: monoclonal antibodies, which act on the external part of the cellular receptor; and tyrosine kinase inhibitors, which act intracellularly. Conclusion: Key studies involving bevacizumab, sunitinib, and sorafenib show that these drugs can have an important role in limiting the growth of a cancer. However, their optimal use with or without conventional chemotherapy must be determined. Key words: vascular endothelial growth factor; angiogenesis; monoclonal antibodies; tyrosine kinase inhibitors; targeted therapy; bevacizumab; sorafenib; sunitinib.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".