A Review of the Recent Advances of Cardiac Pacemaker Technology in Handling Complications
Bibliographic record
Abstract
The total number of annual pacemaker implantations continues to grow globally, and help patients with heart rhythm disorders with an improved quality of life and reduced mortality. The first implantable pacemakers appeared in 1965, characterized by their bulkiness, short battery life, and a single pacing mode. Innovation led to the modern pacemaker: a smaller system with improved battery life and capacity, and innovation in lead technology. Certain arrhythmia conditions may also qualify for leadless pacemaker implantation, thus eliminating the spectrum of complications that could occur with leads. Adverse events can be divided into acute (perforation, lead dislodgement, infection) and long-term (lead fractures, device infection, insulation failure). Traditional long-term complications with leads occur in 10% of patients, compared with device-related adverse effects observed in 6.7% of leadless pacemakers. Furthermore, cardiac pacemaker implantation results in quality of life improvements across all age groups. Large cardiac rehabilitation studies have demonstrated the effectiveness of exercise in reducing the physical complications involved with pacemaker implantation. Of the three randomized controlled trials examined, all of them reported some benefit of exercise in the intervention group compared with the control. The following review aims to discuss the multitude of pacemaker options potentially available for the clinician, complications, their course of management, and the path forward with innovations arising out of previous research within the field.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".