Effect of Modern Pacing Algorithms on Generator Longevity:
Bibliographic record
Abstract
Pulse generator (PG) longevity is of major importance to the quality of care of pacemaker patients. A series of automatic algorithms affect PG longevity. This study investigated the individual and combined effects of three algorithms incorporated in the Medtronic Kappa 700 pacemaker series: Capture Management periodically measures the stimulation threshold and adjusts the PG output, Sinus Preference allows the sinus rate to prevail in a specified range below the sensor rate, and Search AV allows an extension of the AV interval if spontaneous conduction is observed. The effects of Capture Management, Sinus Preference, and Search AV on device longevity were studied in 21 consecutive patients treated in the VDD and DDDR modes. Patients were followed for 1 year. The data were analyzed using an equation provided by the manufacturer. Capture Management was activated in 20 patients. For 11 PGs at the basic settings, longevity was extended by 5.2%, whereas reprogrammed PGs had no gain. Sinus Preference was active in four DDDR patients, who gained 12.0 +/- 5.3%atrial sensing from it, with a resultant longevity gain of1.4 +/- 0.45 months(NS). Search AV was active in 19 patients and 8 responders gained 7.8 +/- 4.4 months PG longevity. The overall longevity in this study was 106.3 +/- 8.4 months with all features as programmed, whereas the longevity without Capture Management and Search AV algorithms would be 98.2 +/- 4.9 months, saving 8.1 +/- 5.8 months(range 0-18) of battery life. Thus, two algorithms: Capture Management and Search AV, have clinical relevance in the extension of PG longevity.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".