P3263North American compliance with the shock reduction programming recommendations
Bibliographic record
Abstract
Background: Implantable cardioverter defibrillator (ICD) shock reduction programming, through increasing the number of intervals needed to detect (NID) and decreasing the treatment and SVT discrimination zone cycle lengths, is recommended to reduce both inappropriate shocks and heart failure hospitalizations with no increase in syncope. We sought to evaluate whether programming practices have changed over time or differ by region or clinic size. Methods: All patients implanted between 2006–2016 with Secura, Virtuoso and Protecta single or dual chamber ICDs from North America followed through remote monitoring surveillance (CARELINK) were included. Data was accessed 11–29–16. The percentage of ICDs programmed to NID ≥30/40 in the VF zone at their most recent and initial CARELINK transmission was documented. This percentage was then stratified by implant year, region (each Canadian Province and Midwest, South, West and New England in the United States), and number of patients per clinic enrolled in CARELINK. Results: 21,556 of the 68,869 (31.3%) patients from 3388 clinics were programmed to an NID ≥30/40 from the most recent remote transmission. The percentage of ICDs programmed to an NID ≥30/40 increased incrementally dependent on implant year (figure). The proportion of ICDs with the NID programmed ≥30/40 differed significantly by region ranging from 25% to 79%. There was no relationship between the number of patients enrolled in CARELINK in each specific clinic and the percentage of patients with an NID ≥30/40.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".