Abstract 2407: Stage shifting by modifying the determinants of cancer
Notice bibliographique
Résumé
Early detection of cancer is a major challenge because most tumors are asymptomatic in the early stages. However, if the cancer is caught early, it can be treated more effectively thereby improving survival rates. Many environmental, lifestyle and genetic risk factors for developing cancer have been identified; however, they collectively do not fully explain its etiology. By using health and lifestyle information of people along with their cancer screening history before they develop cancer, we hope to identify factors that can be modified to avoid diagnosis at later stages of disease. These identified factors will be evaluated immediately using simulated data that mimics real data without waiting years for results from intervention studies. Hence, the aim of our study is to explore patient and health care system factors associated with stage of cancer diagnosis and to explore reduction in stages at diagnosis by modifying identified factors using simulation methods. The study is based on results obtained from the analysis of the data collected from the participants in Alberta Tomorrow Project (ATP), a prospective cohort of over 50,000 adults (aged ≥35 years). More than 3,500 participants have been diagnosed with cancer since their enrollment in the ATP. Close to 80% of the breast and colorectal cancers, and 70% of the lung cancer were diagnosed at early stage (stages I and II) and late stage (stages III and IV), respectively. Using these information as prior knowledge, we simulated the stages of breast, lung, and colorectal cancers at diagnosis along with risk factors and screening interventions. The outcome (stage at diagnosis) was modeled on the ordinal scale (I-IV) using a popular ordinal logistic regression model, so-called proportional odds and partial proportional odds models, that assesses the strengths of the associations between ordinal outcomes and measured risk factors. Preliminary findings from our simulation study for either ordinal response model found that a change in combination of risk factors could increase the probability of shifting the stage of diagnosis. We also plan to explore a shift towards earlier stage at diagnosis by modifying selected environmental and lifestyle risk factors - for example, reducing the amount of time spent in the midday sun (reduced risk exposure), and increase the amount of vitamin D (increase protective factor exposure). This will be done systematically to identify the impact of factors individually and collectively. The comprehensive modeling of factors associated with cancer stage at diagnosis provides information simply not attainable in empirical studies. Using a simple framework of stage at diagnosis, we expect to identify factors that can be used by screening and prevention programs to identify individuals who may benefit from individualized screening practices or from targeted prevention messages, thereby increasing the proportion of cases diagnosed at earlier stages.Citation Format: Gyanendra Pokharel, Paula J. Robson, Lorraine Shack, John J. Spinelli, Karen A. Kopciuk. Stage shifting by modifying the determinants of cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2407.
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) | 0,001 | 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 ».