Remote Patient Monitoring for Patients with Heart Failure: Sex- and Race-based Disparities and Opportunities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it