Delayed AICD therapy and cardiac arrest resulting from undersensing of ventricular fibrillation in a subject with hypertrophic cardiomyopathy–A case report
Bibliographic record
Abstract
Defibrillation testing is no longer routinely performed after automatic implantable cardioverter-defibrillator (AICD) implantation. However, certain subjects undergoing AICD implantation may be at higher risk of undersensing of ventricular arrhythmias resulting in potentially fatal outcomes. We present the case of a 30-year-old woman with hypertrophic cardiomyopathy (HCM; 'asymmetric septal hypertophy' morphologic variant) and prophylactic AICD who experienced an out of hospital cardiac arrest. AICD interrogation revealed undersensing as a result of intermittent high amplitude electrograms during an episode of ventricular fibrillation (VF). The subject underwent replacement and repositioning of the AICD lead along with pulse generator replacement (that utilized a different VF sensing algorithm) with appropriate sensing of VF and successful defibrillation testing. The presence of intermittent high amplitude electrograms during episodes of VF in AICDs using the AGC function should be recognized as a situation that may necessitate interventions to prevent undersensing and consequent delay in therapy.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".