2019 HRS/EHRA/APHRS/LAHRS focused update to 2015 expert consensus statement on optimal implantable cardioverter-defibrillator programming and testing
Bibliographic record
Abstract
The 2015 HRS/EHRA/APHRS/SOLAECE Expert Consensus Statement on Optimal Implantable Cardioverter-Defibrillator Programming and Testing provided guidance on bradycardia programming, tachycardia detection, tachycardia therapy, and defibrillation testing for implantable cardioverter-defibrillator (ICD) patient treatment. The 32 recommendations represented the consensus opinion of the writing group, graded by Class of Recommendation and Level of Evidence. In addition, Appendix B provided manufacturer-specific translations of these recommendations into clinical practice consistent with the recommendations within the parent document. In some instances, programming guided by quality evidence gained from studies performed in devices from some manufacturers was translated such that this programming was approximated in another manufacturer's ICD programming settings. The authors found that the data, although not formally tested, were strong, consistent, and generalizable beyond the specific manufacturer and model of ICD. As expected, because these recommendations represented strategic choices to balance risks, there have been reports in which adverse outcomes were documented with ICDs programmed to Appendix B recommendations. The recommendations have been reviewed and updated to minimize such adverse events. Notably, patients who do not receive unnecessary ICD therapy are not aware of being spared potential harm, whereas patients in whom their ICD failed to treat life-threatening arrhythmias have their event recorded in detail. The revised recommendations employ the principle that the randomized trials and large registry data should guide programming more than anecdotal evidence. These recommendations should not replace the opinion of the treating physician who has considered the patient's clinical status and desired outcome via a shared clinical decision-making process.
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.
How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".