Adapting and Developing an Academic and Community Practice Collaborative Care Model for Metastatic Breast Cancer Care (Project ADAPT): Protocol for an Implementation Science–Based Study
Bibliographic record
Abstract
BACKGROUND: Metastatic breast cancer (MBC) remains incurable despite significant treatment advances. Coordinating care for patients with MBC can be challenging given the various treatment options, available clinical trials, and frequent need for ancillary services. To optimize MBC care, we designed a project for adapting and developing an academic and community practice collaborative care model for MBC care (Project ADAPT), based on the Ending Metastatic Breast Cancer for Everyone (EMBRACE) program developed at Dana Farber Cancer Institute. OBJECTIVE: We aim to describe the implementation science-based study design and innovative components of Project ADAPT. METHODS: Project ADAPT uses the Dynamic Adaptation Process informed by the Exploration, Preparation, Implementation, Sustainment framework. Washington University School of Medicine (WUSM) partnered with 3 community hospitals in the St. Louis region covering rural and urban settings. The exploration and preparation phases provide patient and provider feedback on current referral practices to finalize the approach for the implementation phase. At the implementation phase, we will enroll patients with MBC at these 3 community sites to evaluate potential collaborative care at WUSM and assess the impact of this collaborative care model on referral satisfaction and acceptability for patients with MBC and their providers. Patients may then return to their community site for care or continue to receive part of their care at WUSM. We are incorporating virtual and digital health strategies to improve MBC care coordination in order to minimize patient burden. RESULTS: The exploration phase is ongoing. As of August 2021, we have recruited 21 patient and provider participants to complete surveys of the current collaborative care process at WUSM. Using a 2-tailed paired t test, 44 patients (including 10 patients from the exploration phase) and 32 oncologists are required to detect an effect size of 0.5 with 80% power at a level of significance of .05. Throughout this phase and in preparation for the implementation phase, we have iteratively updated and refined our surveys for the implementation phase based on testing of our data collection instruments. Our partner sites are in various stages of the single institutional review board (IRB) approval process. We have ongoing engagement with all partner sites, which has helped solidify our participant recruitment strategies and design patient-friendly recruitment materials. In addition, we have included a patient advocate on the research team. Members of the research team have launched a single IRB Support Network at WUSM to create a repository of the single IRB procedures in order to streamline the partner site onboarding process and facilitate enhanced collaboration across institutions. CONCLUSIONS: With this robust model, we expect that patients with MBC will receive optimal care regardless of geographical location and the model will improve patient and provider experiences when navigating the health system. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/35736.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.113 | 0.104 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.053 | 0.009 |
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".