Pacemaker Implantation in Small Animal Practice: Indications, Types of pacing and Implantation Technique
Bibliographic record
Abstract
The implantation of pacemakers is widely accepted as a standard procedure for addressing symptomatic bradycardia in both dogs and cats.The most common rhythm irregularities necessitating pacing for the relief of clinical symptoms or the enhancement of survival rates comprise advanced second-and third-degree atrioventricular blocks, sick sinus syndrome, persistent atrial standstill, and vasovagal syncope.A number of dog breeds, including West Highland White Terriers, Miniature Schnauzers, and Cocker Spaniels, are prone to sinus node disease, whereas Labrador retrievers and German shepherds are prone to atrioventricular block.Since its initial use in 1967 on dogs having third-degree heart blocks, implantation has remained a consistent practice in the field of veterinary medicine.Pacing leads and a pacemaker generator are two components of a modern pacemaker system.The current pacemaker installation technique for dogs uses endocardial leads and is placed intravenously, but for cats, thoracotomies or laparotomy are used to place epicardial leads.Depending on the conduction abnormalities, there are many different ways of pacing, which include atrial pacing, right ventricular apex pacing, interventricular septum pacing, right ventricular outflow tract pacing, etc.Beyond its accomplishments in human medicine, it has attained notable success in veterinary practice, benefiting various animals such as dogs, cats, ferrets, donkeys, and others. HIGHLIGHTSm Pacemaker implantation is accepted as a standard procedure for addressing symptomatic bradycardia in both dogs and cats.m Advanced second-and third-degree atrioventricular blocks, sick sinus syndrome, persistent atrial standstill, and vasovagal syncope are the primary rhythm irregularities requiring pacing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".