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.
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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 | 0.001 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".