<b>A Cardiac Evoked Response Algorithm Providing Threshold Tracking</b>: A North American Multicenter Study
Bibliographic record
Abstract
The purpose of this study was to evaluate a pacing system using the recognition of cardiac evoked response for the automatic adjustment of pacing output. Patients were prospectively followed after primary implantation of VVIR pacemakers using AutoCapture (St. Jude Medical CRMD). Sensing and pacing thresholds, polarization signal, evoked response, and AutoCapture performance were evaluated with serial visits and 24-hour Holter monitoring. Three hundred ninety-eight patients (mean age 71 +/- 15 years) were followed for an average duration of 1 year (3 days-1.75 years) with the algorithm functional in > 90% of patients. Backup pacing in the event of exit block was confirmed in all patients. Pacing thresholds remained stable at 0.89 +/- 0.34 V with a pulse width of 0.31 ms (with chronic output autoset at 0.3 V above the actual threshold). Evoked response exhibited a small but statistically significant increase with time (8.92 mV at implant, 9.60 mV at 12 months), however, this finding did not result in any change in AutoCapture function during our follow-up period. The polarization signal remained stable with minimal variation (1.12 mV at implant, 1.18 at 12 months). No clinical adverse events were observed using the AutoCapture algorithm. In this initial experience with the AutoCapture algorithm the evoked response and polarization measurements remained adequate, allowing the system to function in the majority of patients with safe, low output pacing. High energy backup pacing provided an added safety feature over fixed output devices in cases of unexpected threshold rises. Longer follow-up is required for continued long-term validation of the algorithm.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".