Abstract P12: Colorectal cancer screening in Appalachian Kentucky primary care clinics during COVID-19
Notice bibliographique
Résumé
Abstract Background: Colorectal cancer (CRC) mortality is disproportionately higher in Appalachian counties of Kentucky than in non-Appalachian regions. Part of the mortality gap can be explained by lower screening rates in Appalachian counties. Researchers at Markey Cancer Center partnered with primary care clinics in eastern Kentucky to address this disparity by identifying strategies to implement evidence-based interventions (EBIs) to improve CRC screening and follow-up in Appalachian Kentucky. Methods: Members of the research team conducted formative research activities to identify multilevel barriers to CRC screening. A menu of EBIs was then created to address these barriers, and clinic champions selected EBIs that were feasible in their respective practices. However, because of restrictions during COVID-19, clinics experienced multiple changes to workflow and operations, necessitating modifications to program activities. Over a series of virtual meetings, clinic champions selected adaptations that could allow clinics to continue promoting CRC screening in their practices despite COVID-related limitations. Results: Changes in clinic staffing and workflow resulting from COVID-19 included provider furloughs, a state-mandated pause in elective procedures, mandatory parking lot visits for many in-person visits, and an increase in telehealth. Among our clinic partners, total in-person visits were reduced by nearly half from first to second quarter of 2020, whereas telehealth visits were 23 times higher, though telehealth visits were cut in half by third quarter. To match these changing modes of practice, clinics adapted creative strategies for communicating CRC screening recommendations to patients, including shifting from paper to digital educational tools, promoting screening via telehealth visits, and prioritizing recommendations for stool-based tests over colonoscopy for average-risk patients. As a result, orders for FIT and FIT-DNA were 2 and 3 times higher, respectively, from second to third quarter of 2020. Conclusion: Rural primary care clinics in Appalachia continue to promote CRC screening despite the multiple challenges related to COVID-19. One relevant reference for clinicians is the National Colorectal Cancer Roundtable’s playbook for reigniting CRC screening during COVID-19, a document that promotes stool-based screening for average-risk patients. While elective procedures remain backlogged in rural areas due to state regulations, research partners should emphasize the need to prioritize stool-based CRC screening for average-risk populations and reserve scheduling colonoscopies for high-risk individuals or those with abnormal stool-based test results. While our clinical partners had previously focused on a “colonoscopy first” approach to screening, our findings suggest that our clinic partners increased orders for stool-based CRC tests. Nevertheless, continued outreach is needed to ensure CRC screening rates remain optimal. Citation Format: Aaron J. Kruse-Diehr, Mark Cromo, Melinda Rogers, Angela Carman, Bin Huang, David Gross, Sue Russell, Vickie Fairchild, Mark Dignan. Colorectal cancer screening in Appalachian Kentucky primary care clinics during COVID-19 [abstract]. In: Proceedings of the AACR Virtual Meeting: COVID-19 and Cancer; 2021 Feb 3-5. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(6_Suppl):Abstract nr P12.
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 | 0,002 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,005 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 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 ».