Remote Patient Monitoring for Patients with Heart Failure: Sex- and Race-based Disparities and Opportunities
Bibliographic record
Abstract
Remote patient monitoring (RPM), within the larger context of telehealth expansion, has been established as an effective and safe means of care for patients with heart failure (HF) during the recent pandemic. Of the demographic groups, female patients and black patients are underenrolled relative to disease distribution in clinical trials and are under-referred for RPM, including remote haemodynamic monitoring, cardiac implantable electronic devices (CIEDs), wearables and telehealth interventions. The sex- and race-based disparities are multifactorial: stringent clinical trial inclusion criteria, distrust of the medical establishment, poor access to healthcare, socioeconomic inequities, and lack of diversity in clinical trial leadership. Notwithstanding addressing the above factors, RPM has the unique potential to reduce disparities through a combination of implicit bias mitigation and earlier detection and intervention for HF disease progression in disadvantaged groups. This review describes the uptake of remote haemodynamic monitoring, CIEDs and telehealth in female patients and black patients with HF, and discusses aetiologies that may contribute to inequities and strategies to promote health equity.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".