ThrombUS+ D6.1: Product Design Specifications
Bibliographic record
Abstract
Deliverable D6.1 aims to revise the high-level regulatory, technical, and functional requirements outlined in previous deliverables (D2.2, D2.3, and D2.4) and provide detailed specifications for the ThrombUS+ system, designed for monitoring and preventing deep vein thrombosis (DVT).A thorough analysis of the proposed conceptual architecture revealed the need for two critical external components: a dedicated Wi-Fi router for secure, reliable, and low-latency communication between modules, independent of the hospital network, and an isolating power transformer to ensure electrical safety, noise reduction, and compliance with medical standards. These additions were incorporated into the system design. The internal ThrombUS+ system modules were categorized into hardware and software components, with detailed specifications provided in standardized tables. This process involved making several key decisions, and the specifications will serve as a foundation for the development of individual modules and subsystems in the future.The ThrombUS+ system is designed with high modularity, allowing flexible configuration and integration of different modules to support various DVT monitoring methods. The primary monitoring methods include compression duplex ultrasonography, venous occlusion plethysmography, and light reflection rheography. Each method will utilize different module sets and operate in one of three working modes: imaging, monitoring, or analysis. A separate set of modules will also focus on DVT prevention.Finally, the deliverable identifies potential improvements to the ThrombUS+ system development process, including the adoption of embedded systems design methodologies, optimization of real-time embedded execution, risk mitigation strategies, and the possibility of extending the system to support telemonitoring.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.069 | 0.068 |
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".