Biosensors with left ventricular assist devices
Bibliographic record
Abstract
Heart failure imposes a significant global health burden, standing as a primary contributor to mortality. Various indicators and physiological shifts within the body may hint at distinct cardiac conditions. Specific biosensors have the capability to identify these changes. Integrating or embedding these biosensors into mechanical circulatory support devices (MCSDs), such as left ventricular assist devices (LVADs), becomes crucial for monitoring alterations in biochemical and physiological factors subsequent to an MCSD implantation. Detecting abnormal changes early in the course of disease progression will allow for improved patient outcomes and prognosis following an MCSD implantation. The aim of this review is to explore the available biosensors that may be coupled or implanted alongside LVADs to monitor biomarkers and changes in physiological parameters. Different fabrication materials for the biosensors are discussed, including their advantages and disadvantages. This review also examines the feasibility of integrating feedback control mechanisms into LVAD systems using data from the biosensors. Challenges facing this emerging technology and future directions for research and development are outlined as well. The overarching goal is to provide an overview of how implanted biosensors may improve the performance and outcomes of LVADs through continuous monitoring and closed-loop control.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".