Abstract P12: Colorectal cancer screening in Appalachian Kentucky primary care clinics during COVID-19
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".