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PREDICTORS OF EPI PROCOLON UTILIZATION

2022· article· en· W7005230175 sur OpenAlex

Pourquoi ce travail est dans la base

Une base qui oublie comment elle a trouvé un travail ne peut pas être vérifiée. Voici les voies qui ont admis celui-ci.

aboutLe titre ou le résumé porte un signal canadien du lexique géographique.
no affAucune affiliation canadienne : ce travail est invisible pour une base fondée sur la seule affiliation.
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Notice bibliographique

RevueJournal of Media Literacy Education · 2022
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueIchthyology and Marine Biology
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésColorectal cancerCohortCancerPopulationRisk factorCohort studyQuarter (Canadian coin)Food and drug administrationBehavioral Risk Factor Surveillance System
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Background: According to the American Cancer Society, despite being one of the most preventable forms of cancer, colorectal cancer (CRC) is the fourth most common type of cancer and is the second leading cause of cancer-related deaths in the United States (US). The 2020 Behavioral Risk Factor Surveillance System estimates that around one quarter of the US population aged 50 to 75 and over one half of those in their early 50s remain unscreened despite being at risk. In 2016, the United State Food and Drug Administration approved a blood test for CRC screening called Epi proColon. Epi proColon is intended for patients who are at risk of developing CRC and have declined other existing forms of screening. As such, it could help increase CRC screening among at-risk patients who would otherwise not adhere to screening recommendations.\nObjective: The objective of the study was to determine predictors of Epi proColon utilization among insured patients aged 50 to 55 with no previous history of CRC screening, diagnosis, or total colectomy.\nMethods: A matched case-control study was conducted using de-identified data from the ClinformaticsTM® DataMart, an administrative health claims database. Medical claims were used to identify cases of Epi proColon utilization in 2017. Using risk-set sampling, cases were randomly matched to 10 controls based on the index month of each case. Patients were excluded from the study cohort if they had previously been screened for CRC, were diagnosed with CRC, had history of a total colectomy, did not have a recent preventive health visit, or did not meet continuous enrollment criteria. A final study population was 935 patients, 85 cases and 850 controls. Significant predictors (age, race/ethnicity, income level, education level, and geographic region) of Epi proColon utilization were determined using multivariable logistic regression analysis to calculate odds ratios (OR) and corresponding 95% confidence intervals (CI).\nResults: We identified 5 demographic and socioeconomic characteristics that were statistically significant predictors of Epi proColon utilization. Age was found to increase the likelihood of Epi proColon utilization by 1.174 times for each increasing year of age (95% CI 1.023 – 1.349, p-value = 0.0228). Patients who were identified as Hispanic were 2.019 times more likely to use Epi proColon when compared to those identified as White (95% CI 1.030 – 3.960, p-value = 0.409). Patients with high school education or less were 1.818 times more likely to use Epi proColon when compared to those with some college education or greater (95% CI 1.098 – 3.010, p-value = 0.0201). Patients with household incomes of less than $40,000 annually were 2.187 times more likely to use Epi proColon when compared to those with household incomes $40,000 or greater (95% CI 1.209 – 3.958, p-value = 0.0097). Compared with patients from the Northwest, Midwest, or with unknown geographic regions, patients from the South were 3.070 times more likely (95% CI 1.619 – 5.821, p-value = 0.0006) and patients from the West were 2.340 times more likely (95% CI 1.098 – 4.987, p-value = 0.0276) to use Epi proColon.\nConclusion: The findings from this study suggest that Epi proColon may help to increase CRC screening rates in Hispanic patients, low-income patients, and those with less education. As such, it could be used to improve CRC outcomes among these groups that have higher rates of non-adherence to CRC screening recommendations. Increasing age was also predictive of utilization. This information can be helpful for public health officials, providers, and advocacy groups in developing strategies to address disparities in CRC screening and outcomes.

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.

Prédiction distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,191
Score d'incertitude au seuil0,998

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0030,000

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.

Tête enseignante Opus0,010
Tête enseignante GPT0,274
Écart entre enseignants0,265 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle