Improving evidence-based treatment selection and patient-centered care in upper GI cancers: A Project ECHO initiative.
Bibliographic record
Abstract
4051 Background: Accurate assessment of biomarkers (including HER2 and PD-L1) is integral to treatment selection in upper gastrointestinal (GI) cancers. However, challenges in integrating biomarker testing and targeted therapies are commonly reported in community settings. The Project Extension for Community Healthcare Outcomes (Project ECHO) model addresses this gap by connecting experts in tertiary care settings with rural healthcare teams. Through case-based teaching, the model builds the knowledge and skills needed to provide evidence-based, equitable care regardless of geographic location. Methods: In October 2024, 57 healthcare professionals (HCPs) from 2 US and 4 Canadian community oncology clinics participated in Project ECHO sessions. Led by an expert oncologist, each session featured interactive discussions of real-world anonymized case presentations to address key practice gaps in integrating biomarker-based therapies and coordinating multidisciplinary care for patients (pts) with upper GI cancers. Following each session, HCPs developed and implemented site-specific action plans to address gaps in care. Pre-activity and post-activity surveys measured the impact on knowledge, confidence, and competence and 90-day follow-up surveys will be collected to assess ongoing performance. Results: The top HCP-reported barriers to individualized care for pts with upper GI cancer included keeping up with the latest efficacy and safety data (44%), selecting and sequencing treatments based on individual and disease factors (40%), and limited availability/cost of biomarker testing (39%). Additionally, relatively few HCPs reported providing supportive care services for the majority of their patients, such as palliative care referrals (40%), distress screening (26%), psychosocial support (35%), and end of life counseling (19%). Following the Project ECHO sessions, HCPs demonstrated improved knowledge, competence, and confidence in biomarker testing and managing adverse events. Additionally, HCPs planned to increase patient education about disease and treatment-related side effects (65%), improve team education on biomarker testing and treatment selection (61%), establish standardized biomarker testing protocols (35%), and increase supportive care referrals (22%). Team action plans included implementing routine testing protocols, establishing a network of specialists to coordinate care, and increasing education on the use of immunotherapy. Conclusions: Project ECHO-based education improved HCP capacity to integrate biomarker-directed therapies and coordinate multidisciplinary care for patients with upper GI cancers. Full findings will detail long-term impacts on practice and inform future community-based ECHO initiatives.
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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 | 0.089 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.004 | 0.005 |
| 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".