TreC_Metha: A Digital Application to Enhance Patient Agency, Therapy Compliance and Quality of Life in Metastatic Breast Cancer Patients
Bibliographic record
Abstract
The prognosis for Hormonal Receptor positive-HER2-negative (HR+ HER2-negative) metastatic breast cancer (mBC) has significantly improved by advances in hormone therapies, targeted drugs, and antibody-drug conjugates (ADCs). Nevertheless, maintaining quality of life (QoL), managing symptoms, and reducing treatment-related toxicity remain essential. BACKGROUND: eHealth solutions offer new opportunities to enhance patient engagement and well-being through digital tools. This paper aims to delineate the fundamental functionalities and objectives of TreC_Metha, a technologically advanced instrument to provide effective support during all care process of patients diagnosed with HR+HER2-negative mBC able to proactively change its configuration depending on the treatment line or on the intra-line treatment phase the patient undergoes, as set by the healthcare team. METHODS: The TreC_Metha platform was developed through a structured, evidence-based four-phase process aimed at scalability, usability, and clinical relevance. The development began with a formal analysis of the metastatic breast cancer (mBC) care pathway using BPMN modeling to map phases, activities, and stakeholders, highlighting differences from early-stage breast cancer. This analysis informed the identification of key points where digital support could enhance care. Patient needs were assessed through a web-based questionnaire (N = 20) and two focus groups (N = 11), enabling a participatory design approach. Based on these insights, the platform's functional and non-functional requirements were defined, leading to the design and implementation of a patient-facing mobile app and a clinical dashboard tailored to mBC-specific needs. RESULTS: Preliminary findings from the web survey focus groups revealed significant gaps in communication and information delivery during the mBC care journey, contributing to patient anxiety and reduced confidence. Participants expressed a preference for digital and printed resources to improve understanding and facilitate interactions with healthcare providers. These insights informed the development of the TreC_Metha platform. The clinical dashboard enables real-time monitoring and decision-making, while the mobile app supports bidirectional communication, therapy adherence, and patient-reported data collection. A system prototype is currently under refinement and will undergo usability testing with a small cohort of users. Following this phase, the pilot study will evaluate the platform's impact on QoL, aiming for a ≥10% improvement in outcome measures and contributing to a more patient-centered care model in the mBC setting. CONCLUSIONS: TreC_Metha represents an innovative tool that may enable involvement and active participation in the mBC care process for both a multidisciplinary care team of professionals and the patient, and that can be easily adapted to other cancer types and chronic diseases.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".