The Subcutaneous Implantable Cardioverter-Defibrillator: Not For All
Bibliographic record
Abstract
Implantable cardioverter-defibrillators (ICDs) have a proven role in primary and secondary prevention of sudden death from ventricular arrhythmias. Transvenous ICD systems despite this proven benefit, have been plagued by lead problems including fracture, insulation breaks and recalls; and issues with transvenous access including pneumothorax and venous obstruction. Infected or fractured leads require full extraction which may require laser-assisted removal which carry a mortality and morbidity risk. Subcutaneous ICDs (S-ICD) are an exciting recently approved technology providing the advantages of effective shock rescue without need for transvenous leads. They function as essentially shock boxes without pacing capability except for a short period of post shock transcutaneous pacing. This technology provides an emerging option especially in patient groups who do not have a pacing indication, have no venous access, patients at high infection risk, patients with no structural heart disease and hereditary arrhythmias; and patients with congenital heart disease that precludes transvenous leads. Patients with slow monomorphic ventricular tachycardia consistently terminated by antitachycardia pacing are also not candidates for this therapy in evolution. In addition, S-ICDs do not have remote monitoring capability.
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 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.038 | 0.019 |
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".