ISHNE/EHRA expert consensus on remote monitoring of cardiovascular implantable electronic devices (CIEDs)
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
We are in the midst of a rapidly evolving era of technology-assisted medicine. The field of telemedicine provides the opportunity for highly individualized medical management in a way that has never been possible before. Evolving medical technologies using cardiac implantable devices (CIEDs) with capabilities for remote monitoring permit evaluation of multiple parameters of cardiovascular physiology and risk, including cardiac rhythm, device function, blood pressure values, the presence of myocardial ischaemia, and the degree of compensation of congestive heart failure. Cardiac risk, device status, and response to therapies can now be assessed with these electronic systems of detection and reporting. This document reflects the extensive experience from investigators and innovators around the world who are shaping the evolution of this rapidly expanding field, focusing in particular on implantable pacemakers (IPGs), implantable cardioverter-defibrillators (ICDs), devices for cardiac resynchronization therapy (CRT) (both, with and without defibrillation properties), loop recorders, and haemodynamic monitoring devices. This document covers the basic methodologies, guidelines for their use, experience with existing applications, and the legal and reimbursement aspects associated with their use. To adequately cover this important emerging topic, the International Society for Holter and Noninvasive Electrocardiology (ISHNE) and the European Heart Rhythm Association (EHRA) combined their expertise in this field. We hope that the development of this field can contribute to improve care of our cardiovascular patients.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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