Abstract 990: Fundamental causes of colorectal cancer outcomes
Notice bibliographique
Résumé
Abstract Introduction Colorectal Cancer is a major cause of mortality with 16.2 people out of 10,000 dying in 2009. Treatments for colorectal cancer exist, with screening done by physicians at clinics and hospitals around the US. Social inequalities in Colorectal Cancer mortality are well studied. However, three theories have arisen that may help to explain why these inequalities have arisen. Fundamental Cause Theory posits that these inequalities arise due to unequal access to resources while this may work in part with differential access to healthcare, and finally differential Diffusion of Knowledge is posited to speed and slow uptake of new medical innovations. Method Using administrative and census data from 2005, mortality rates per county in 3139 counties were stratified by socio-economic status (SES), volume of acute care hospitals (ACHs), volume of primary care physicians (PCPs), and groups of states considered slow to fast diffusion. We controlled for race and gender. Preliminary Results There were 4,683 ACHs and 278,961 PCPs in the analyses. White male averaged 6.42 deaths per 10,000 people, White female at 4.27, Black male at 4.42, Black female at 3.24, Other male at 2.14, and Other female at 2.93. For hospital volume, the average mortality for counties with zero acute hospitals was 7.32 deaths per 10,000, one hospital was 6.20, two hospitals was 6.44, three to fifty hospitals was 5.62, and fifty-five to eighty-nine hospitals was 4.55. For counties with PCPs, areas with zero to four PCPs had an average mortality of 7.48 deaths per 10,000 people, five to thirteen PCPs was 6.06, fourteen to fifty PCPs was 6.17, and fifty-one to eight thousand eight hundred sixty three PCPs was 5.93. The average mortality for PCPs per 100,000 between 0-50 was 7.16 per 10,000, 51-100 was 6.10, 101-149 was 5.78, and more than 150 was 5.08. Counties with high SES and few hospitals had an average mortality of 6.64 per 10,000, where high SES and high volume of hospitals had 5.62, low SES and low volume had 6.19, and low SES and high volume had 7.34. States with slow diffusion had a mortality of 6.33 per 10,000, medium-slow had 6.13, medium-fast had 6.61, and fast had 5.86. Conclusion In this study, we show varying support for each of the three major theories. Fundamental cause theory suggests that SES was correlated with lower mortality rates, but SES played the greatest role in counties with large numbers of hospitals and primary care physicians. Access to healthcare clearly mattered, with more hospitals and primary care physicians correlating to lower colorectal mortality rates. Finally, being in an area typified as quick diffusing was related to lower mortality. This study thus suggests that fundamental cause theory works in part through access to health care. There are long term implications for policy makers looking to reduce social inequalities in colorectal cancer mortality. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 990.
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 | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 0,001 |
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 ».