Developing evidence‐based, cost‐effective <scp>P4</scp> cancer medicine for driving innovation in prevention, therapeutics, patient care and reducing healthcare inequalities
Notice bibliographique
Résumé
The cancer problem is expanding, particularly in low- and middle-income countries (LMICs). Preventive measures can reduce the incidence by 40-50%, and cure rates have increased during the past decades in a number of cancers. However, optimizing prevention programmes and increasing cure rates of cancer remain significant research challenges. The main focus of the conference was on P4 Cancer Medicine (Predictive, Preventive, Personalized and Participatory), a comprehensive strategy encompassing Health-Related Quality of Life (HRQoL) research, aiming to enhance the well-being of patients and individuals at risk. Addressing the cancer problem requires two key elements: translational cancer research and the development of relevant infrastructures. A Comprehensive Cancer Centre (CCC) acts as an innovation hub by integrating high-quality, multidisciplinary therapy and care, with healthcare-dependent prevention, research, and education. The United States has been at the forefront, providing quality-assured CCCs and the Cancer Moonshot for strategic cancer research. The EU has followed with the European Research Council for basic research, the European Innovation Council to boost disruptive innovation, and two EU initiatives on cancer, Europe's Beating Cancer Plan (EBCP) and the Mission on Cancer. The increasing complexity of cancer biology and technologies presents both a research challenge and a healthcare demand. For most patients, a CCC is not available. A critical discussion focused on quality assurance of healthcare outside the catchment area of a CCC and involving patients in clinical research. The strategic deployment of resources to support collective healthcare efforts and research aimed at reducing the cancer problem was discussed with representatives from the United States, EU, Africa, China, India and Taiwan. Analyses of translational cancer research have revealed important gaps in implementing innovations, assessment of clinical effectiveness, HRQoL, outcome and health economics research. The increased release of new anticancer agents over the last 25 years, accompanied by insufficient information on clinical benefits, presents both an economic and ethical problem. Direct healthcare costs have increased due to expenses for anticancer agents for the treatment of patients with incurable diseases. Evidence-based treatment based on HRQoL research is an unmet need. Basic/preclinical research aimed at increasing the cure rate should identify new, broader targets for therapy and develop extended diagnostic technologies for stratifying patients, to inform innovative clinical trials. Present research strategies convert cancer to a chronic disease, a growing burden for the healthcare systems. The increasing complexity of cancer biology and technology, the growing need for translational cancer research, and the demand for supporting infrastructures underscore the importance of international collaborations between CCCs. However, funding for cancer research is not currently aligned to reduce the cancer problem. While public funding for cancer research doubled between 2005 and 2024, the pharmaceutical industry's spending on cancer research increased tenfold. Increasing funding by public and non-profit funding organizations is mandatory. Education is another significant need, but it is currently fragmented and underfunded. The last session of the conference summarized the strategies in a Statement with a strong emphasis on global collaboration addressing the growing cancer burden and pronounced inequalities. Expanding partnerships and fostering innovative, multidisciplinary approaches to cancer prevention, therapeutics/care, as well as research, are not just urgent but essential steps towards reducing incidence, increasing cure rates and enhancing the well-being of cancer patients. Data-driven cancer medicine is currently under development, and modern communication technologies for diagnostics may facilitate interactions across geographical distances. A global cancer research agenda can become a model of solidarity, sustainability, and ethical responsibility.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».