At-home Breast Oncology care Delivered with EHealth solutions (ABODE) study protocol: a randomised controlled trial
Bibliographic record
Abstract
INTRODUCTION: The COVID-19 pandemic disrupted healthcare delivery for patients with breast cancer. eHealth solutions enable remote care and may improve patient activation, which is defined as having the knowledge, skills and confidence to manage one's health. Thus, we developed the Breast Cancer Treatment Application (app) for patients and practitioners to use throughout the cancer care continuum. The app facilitates virtual assistance, delivers educational resources, collects patient-reported outcome measures and provides individualised support via volunteer e-coaches. Among newly diagnosed patients with breast cancer, we will compare changes in patient activation, other patient-reported outcomes and health service outcomes over 1 year between those using the app and Fitbit, and those receiving standard care and Fitbit only. METHODS AND ANALYSIS: This randomised controlled trial will include 200 patients with breast cancer seen at a tertiary care cancer centre in Ontario, Canada. The intervention group (n=100) will use the app in addition to standard care and Fitbit for 13 months following diagnosis. The control group (n=100) will receive standard care and Fitbit only. Patients will complete questionnaires at enrolment, 6 and 12 months post-diagnosis to measure patient activation (Patient Activation Measure-13 score), distress, anxiety, quality of life and experiences with their care and information received. All patients will also receive Fitbits to measure activity and heart rate. We will also measure wait times and number of visits to ambulatory care services to understand the impact of the app on the use of in-person services. ETHICS AND DISSEMINATION: Ethics approval was obtained on 6 January 2023. Protocol version 2.0 was approved on 6 January 2023. The trial is registered with ClinicalTrials.gov. Study findings will be disseminated via publication in a peer-reviewed journal and shared with participants, patient programmes and cancer awareness groups. The app has also been approved as a secure communication method at our trial institution, thus we are well-positioned to support future integration of the app into standard care through collaboration with our hospital network. TRIAL REGISTRATION NUMBER: NCT05989477.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.050 | 0.008 |
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".