Powering the future: Exploring self‐charging cardiac implantable electronic devices and the Qi revolution
Bibliographic record
Abstract
The incidence and prevalence of cardiovascular diseases (CVD) have risen over the last few decades worldwide, resulting in a cost burden to healthcare systems and increasingly complex procedures. Among many strategies for treating heart diseases, treating arrhythmias using cardiac implantable electronic devices (CIEDs) has been shown to improve quality of life and reduce the incidence of sudden cardiac death. The battery-powered CIEDs have the inherent challenge of regular battery replacements depending upon energy usage for their programmed tasks. Nanogenerator-based energy harvesters have been extensively studied, developed, and optimized continuously in recent years to overcome this challenge owing to their merits of self-powering abilities and good biocompatibility. Although these nanogenerators and others currently used in energy harvesters, such as biofuel cells (BFCs) exhibit an infinite spectrum of uses for this novel technology, their demerits should not be dismissed. Despite the emergence of Qi wireless power transfer (WPT) has revolutionized the technological world, its application in CIEDs has yet to be studied well. This review outlines the working principles and applications of currently employed energy harvesters to provide a preliminary exploration of CIEDs based on Qi WPT, which may be a promising technology for the next generation of functionalized CIEDs.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".