Predicting defibrillation outcome based on phase of ventricular activity during ICD implantation
Bibliographic record
Abstract
Implantable cardioverter-defibrillators (ICDs) are well known medical device for patients who are at a risk of sudden cardiac death caused by ventricular fibrillation (VF). The relationship between VF mechanisms and successful ICD therapy to terminate of VF is still not well understood. The purpose of this work is to evaluate the timing of ICD therapy as a predictor of successful VF termination. Clinical data sets were recorded from the patients who underwent ICD implantation in 6 Canadian centers. Timing of the defibrillation attempt (phase) was analyzed by using the ICD Marker Channel which monitors and displays cardiac events sensed by ICD. Phase, based on the VF period, was divided into 10 equally distributed bins and number of successful defibrillation episodes in each bin was compared. A total of 187 defibrillation attempts were identified from the 65 subjects. 126 of the defibrillation attempts were successful, while 61 failed. The optimal case was observed at a phase value of 1.2pi with 2 successful attempts. The lowest performance rate was found at a phase value of 1.4pi and 1.8pi with 50% (3 and 2 successful attempts, respectively). The probability of success was analyzed by using generalized estimating equations (GEE) approach with an exchangeable correlation structure. The results of the GEE logistic regression model indicate no correlation between successful defibrillation attempts and phase of ventricular activity during VF (p-value = 0.78). From our results, timing of defibrillation shock attempt is not a factor in successful termination of VF for patients undergoing ICD implantation.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".