Telemedicine expands cardiovascular care in China – lessons for health equity in the United States
Bibliographic record
Abstract
Unequal access to high-quality healthcare remains one of the most urgent health concerns within the United States 1 and throughout the world 2 . Inequities in access to healthcare worsen health and contribute to over $300 billion in excess healthcare spending in the U.S 3 . Given the health and economic impacts of inequitable access to healthcare, identifying solutions for addressing these inequities is critical. One proposed solution for improving the unequal distribution of healthcare resources is expansion of telemedicine 4 , 5 , defined by the World Health Organization as “the delivery of healthcare services over distance” 6 . While there are disparities in patient use of telemedicine 7 , the overall potential of telemedicine is substantial; telemedicine enables patients in resource-poor settings across the world to receive care from clinicians in resource-rich settings thousands of miles away 8 . A prime example of the benefits of telemedicine is demonstrated in cardiovascular care 9 . In Liu et al.’s recent study “Improving access to cardiovascular care for 1.4 billion people in China using telehealth,” patients used telemedicine to consult providers outside of their province about their cardiovascular concerns, with patients in poorer areas contacting physicians in regions where China’s top hospitals are located 10 . These findings validate the utility of telemedicine for expanding cardiovascular care within developing countries, while drawing attention to limitations of telemedicine’s effects on health equity within the U.S.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.014 | 0.018 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".