Advancing global equity in cardiac care as cardiac implantable electronic device reuse comes of age
Bibliographic record
Abstract
A nation's health and economic development are inextricably and synergistically connected. Stark differences exist between wealthy and developing nations in the use of cardiac implantable electronic devices (CIEDs). Cardiovascular disease is now the leading cause of death in low- and middle-income countries (LMIC), with a significant burden from rhythm-related diseases. As science, technology, education, and regulatory frameworks have improved, CIED recycling for exportation and reuse in LMIC has become possible and primed for widespread adoption. In our manuscript, we outline the science and regulatory pathways regarding CIED reuse. We propose a pathway to advance this technology that includes creating a task force to establish standards for CIED reuse, leveraging professional organizations in areas of need to foster the professional skills for CIED reuse, collaborating with regulatory agencies to create more efficient regulatory expectations and bring the concept to scale, and establishing a global CIED reuse registry for quality assurance and future science.
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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 | 0.030 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.012 | 0.020 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".