REBECCA: A System for Real World Behavioural Data in Breast Cancer Clinical Research and Patient Care
Bibliographic record
Abstract
The REBECCA platform is a digital, cloud-based system designed to support clinical research and patient treatment in breast cancer patients (BCPs) through continuous, real-time monitoring of their behavioural, physical, and emotional well-being.It integrates heterogeneous data sources-wearable sensors, mobile applications, electronic questionnaires, web browsing behaviour, social media interactions, and electronic health records (EHRs)-into a unified and scalable infrastructure.At the core of the platform lies a robust system architecture that combines asynchronous communication via a message broker with distributed data streaming technologies, enabling high-frequency data ingestion and processing.Data from wearable devices and patient-reported outcomes are collected through a mobile application, while EHRs are securely transferred from REDCap servers located at clinical centres.All collected data are stored in secure cloud databases, forming datasets that reflects the patient's lifestyle, treatment journey, and psychosocial status.The platform features an interactive web-based clinical dashboard that allows healthcare professionals to assess individual patient trajectories, evaluate treatment and post treatment impacts, and derive actionable insights for care optimization and research.As a tool for research, it can complement traditional Randomised Control Trials (RCTs).The REBECCA system enables continuous monitoring, predictive modelling, and personalized decision support, integrating real-world data streams and supporting asynchronous data communication among analytic services.This paper presents the system architecture, data integration pipeline, and core functionalities of the REBECCA platform, highlighting its role as an enabler for next-generation clinical monitoring in oncology research and patient-centred care.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.010 |
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".