Optimal Pacing for Symptomatic AV Block:
Bibliographic record
Abstract
VDD pacing provides the physiological benefits of atrioventricular synchronous pacing with the convenience of a single lead system, but is hampered by uncertainty regarding long term atrial sensing and potential development of sinus node disease. To examine the long-term reliability and complication rates of VDD pacing, we compared the outcome of 112 consecutive patients (age 70 +/- 13 years, 59% men) with symptomatic AV block who received a single pass bipolar VDD system, to 80 patients (age 63 +/- 16 years, 70% men) who received DDD pacing for the same indication. All patients were judged to have intact sinus node function based on submitted ECGs and monitoring results at the time of implant. Implant time was reduced in VDD patients compared to DDD patients (63 +/- 20 vs 97 +/- 36 minutes, P < 0.0001). Implant complications occurred in 5 (6%) DDD patients compared to 3 (3%) VDD patients (P = 0.15). The implant P wave was lower with VDD pacing compared to DDD patients (2.91 +/- 1.48 vs 4.0 +/- 1.7 mv, P < 0.0001), but remained stable during long-term follow-up in both groups. During 17.7 +/- 10.0 months of follow-up in the VDD group, only two VDD patients were reprogrammed to VVIR mode, compared to three DDD patients. Physiological atrioventricular activation was maintained in 94%-99% of beats throughout the follow-up period in the VDD group. VDD pacing is an excellent strategy for treatment of patients with symptomatic AV block. The lower cost, high reliability, and abbreviated implantation time suggest that VDD pacing is a viable alternative to DDD pacing in patients with high degree AV block and normal sinus node function.
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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 | 0.000 | 0.002 |
| 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".