Impact of Adaptive Rate Pacing Controlled by a Right Ventricular Impedance Sensor on Cardiac Output in Response to Exercise
Bibliographic record
Abstract
This study examined the effects of adaptive rate pacing controlled by closed-loop right ventricular impedance sensing on exercise hemodynamics. Twelve patients in whom Biotronik INOS2+ pacemakers had been implanted 4-6 weeks earlier participated in the study. All patients completed two graded, symptom-limited exercise tests. The pacemaker was programmed to DDDR with an upper rate limit of 75-85% of the age-predicted maximum heart rate and a lower rate limit of 45-60 ppm. Heart rate was recorded continuously. An average of 5 beats during the last 10 seconds of each exercise stage was used in the analysis. Oxygen uptake (VO2) was measured using open circuit spirometry. The VO2 values from the final 15 seconds of each exercise stage were used for analysis. Stroke volume and cardiac output were measured during the last minute of each stage using impedance cardiography. The test-retest reliability of heart rate and cardiac output responses to graded exercise was assessed using repeated measures analysis of variance, for which the reliability coefficients were r = 0.993 and r = 0.954, respectively (P < 0.01). There were significant correlations (P < 0.01) between VO2 and heart rate and between VO2 and cardiac output, with correlation coefficients of r = 0.907 and r = 0.824, respectively. This method of adaptive rate pacing produced reliable, positive hemodynamic responses to graded exercise on a test-retest basis.
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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 | 0.004 |
| 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".