Notice bibliographique
Résumé
In this article, featured in the CEBP Focus on Cancer Survivorship Research, Parry and colleagues report on the confluence of the increased size and higher age of the cancer survivor population. The authors collected cancer incidence and prevalence data from 9 registries through the Surveillance, Epidemiology, and End Results Program. They report that, as of January 2008, the number of cancer survivors is estimated at 11.9 million. In addition, approximately 60% of these survivors are age 65 or older, and by the year 2020, anestimated 63% of cancer survivors will be 65 or older. This important study shows the convergence of improved cancer survival and population aging, resulting in a growing population of older adult cancer survivors with unique survivorship needs.Considerable interindividual variability exists with regard to the risk of developing an adverse outcome for a given cancer therapeutic. Bhatia presents an overview of the role of genomic variation in the risk of therapyrelated complications. The article discusses common outcomes associated with therapeutic exposures, including cardiomyopathy, obesity, osteonecrosis, ototoxicity, and subsequent malignancies. Issues such as study design, definition of endpoints, and a reliable plan for collecting and maintaining highquality DNA samples are important factors for determining how genetic variation contributes to cancer therapy-related complications.Fatalistic beliefs about cancer have been implicated in low uptake of screening and delays in presentation, particularly in individuals with low socioeconomic status (SES). To explore the interrelationship among SES, fatalism, and early cancer detection behaviors, Beeken and colleagues interviewed adults in the United Kingdom. They report that fatalism was associated with being less positive about early cancer detection and more fearful about seeking help for a suspicious symptom. The authors also found that lower SES groups were more fatalistic. This study promotes addressing fatalistic beliefs about cancer, which might be particularly important for lower SES groups.Indigenous populations in Canada and abroad have poorer survival after a breast cancer diagnosis compared with their geographical counterparts. To explore the reasons for this disparity, Sheppard and colleagues used the Ontario Cancer Registry to compare survival after diagnosis in First Nations (FN) women with that of Non-FN women. Although the authors found that survival was more than 3 times poorer for FN women diagnosed at stage I, compared with non-FN women, the risk of death after a breast cancer diagnosis was nearly 5 times higher among FN women with a comorbidity. These findings suggest that having a preexisting comorbidity was the most important factor in explaining the breast cancer survival disparity among FN women.
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 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 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) | 0,005 | 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 ».