Clinical Cancer Advances 2018: Annual Report on Progress Against Cancer From the American Society of Clinical Oncology
Notice bibliographique
Résumé
A MESSAGE FROM ASCO'S PRESIDENT I remember when ASCO first conceived of publishing an annual report on the most transformative research occurring in cancer care. Thirteen reports later, the progress we have chronicled is remarkable, and this year is no different. The research featured in ASCO's Clinical Cancer Advances 2018 report underscores the impressive gains in our understanding of cancer and in our ability to tailor treatments to tumors' genetic makeup. The ASCO 2018 Advance of the Year, adoptive cell immunotherapy, allows clinicians to genetically reprogram patients' own immune cells to find and attack cancer cells throughout the body. Chimeric antigen receptor (CAR) T-cell therapy-a type of adoptive cell immunotherapy-has led to remarkable results in young patients with acute lymphoblastic leukemia (ALL) and in adults with lymphoma and multiple myeloma. Researchers are also exploring this approach in other types of cancer. This advance would not be possible without robust federal investment in cancer research. The first clinical trial of CAR T-cell therapy in children with ALL was funded, in part, by grants from the National Cancer Institute (NCI), and researchers at the NCI Center for Cancer Research were the first to report on possible CAR T-cell therapy for multiple myeloma. These discoveries follow decades of prior research on immunology and cancer biology, much of which was supported by federal dollars. In fact, many advances that are highlighted in the 2018 Clinical Cancer Advances report were made possible thanks to our nation's support for biomedical research. Funding from the US National Institutes of Health and the NCI helps researchers pursue critical patient care questions and addresses vital, unmet needs that private industry has little incentive to take on. Federally supported cancer research generates the biomedical innovations that fuel the development and availability of new and improved treatments for patients. We need sustained federal research investment to accelerate the discovery of the next generation of cancer treatments. Another major trend in this year's report is progress in precision medicine approaches to treat cancer. Although precision medicine offers promise to people with cancer and their families, that promise is only as good as our ability to make these treatments available to all patients. My presidential theme, "Delivering Discoveries: Expanding the Reach of Precision Medicine," focuses on tackling this formidable challenge so that new targeted therapies are accessible to anyone who faces a cancer diagnosis. By improving access to high-quality care, harnessing big data on patient outcomes from across the globe, and pursuing innovative clinical trials, I am optimistic that we will speed the delivery of these most promising treatments to more patients. Sincerely, Bruce E. Johnson, FASCO ASCO President, 2017 to 2018.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,009 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,009 | 0,004 |
| Science ouverte | 0,003 | 0,005 |
| Intégrité de la recherche | 0,008 | 0,013 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,075 | 0,086 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».