VITALISE D6.3 Summary of the performed activities for JRA2
Bibliographic record
Abstract
This document describes the activities carried out within the context of JRA2 – Transitional Care, focusing on four case studies: a simulation phase pilot conducted in Finland, and three small-scale pilot studies conducted in Greece, Canada and Spain. It provides a detailed account of each phase and activity undertaken by each case study, including pilot definition, execution phase, evaluation, results and conclusions. Furthermore, it presents a harmonized analysis across Living Labs in transitional care, addressing aspects such as the services offered by the Living Labs, their utilization in the pilots, and how their use can contribute to such studies. In healthcare innovation, involving Living Lab creates inclusive environments that involves various stakeholders, aligning regulatory standards, clinical protocols, and human factors to develop effective transitional care solutions based on digital solutions. Capacity building initiatives and collaborative sessions engage diverse stakeholders, while simulation tests and real-world experiments refine interventions iteratively, supported by data collection methods to predict outcomes and ensure compliance with legal and safety standards throughout the innovation process. A comparative analysis has been conducted on the devices used and the data collected by them, as well as the PROMs/PREMs gathered in the context of the JRA2. Additionally, an economic impact assessment has been carried out, evaluating data collection in Living Labs for transitional care and compares home monitoring devices to clinical tools, addressing patient transition challenges. Case studies aim to streamline care and reduce readmission costs through real-time monitoring, offering insights for policymakers to improve EU healthcare systems and lower expenses. Finally, a series of recommendations regarding practices in Transitional Care are provided: engaging patients and clinicians in the design phase ensures that home monitoring technologies effectively meet real-world needs, while standardized protocols and international pilot studies guarantee data consistency and applicability; thorough training for medical personnel in adopting new technologies is key for their smooth integration into clinical practice; supporting patient recovery with user-friendly interfaces and continuous feedback mechanisms for optimal transitional care management.
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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 | 0.022 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.193 | 0.105 |
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".