Transvenous Electrical Cardioversion of Equine Atrial Fibrillation: Patient Factors and Clinical Results in 72 Treatment Episodes
Bibliographic record
Abstract
BACKGROUND: Transvenous electrical cardioversion (TVEC) has been developed for treatment of atrial fibrillation (AF) in horses. The relationship among patient variables, treatment response, and outcome in a typical referral population has not been evaluated. HYPOTHESIS: Patient variables such as age, sex, weight, and duration of arrhythmia affect prognosis for response to treatment and the energy level at which cardioversion occurs. ANIMALS: TVEC was applied to 72 episodes of lone AF in 63 client-owned performance horses, with the majority (54) being Standardbred racehorses. METHODS: Catheterization of the right atrium (RA) and pulmonary artery (PA) through the jugular vein was used for electrode placement before horses were placed under general anesthesia. Biphasic, truncated exponential shock waves were delivered at incremental energy levels until cardioversion was achieved or a maximum single-energy level of 300 J was reached (cumulative energy 50-1,960 J). A multivariate model was constructed to evaluate influence of patient factors on cardioversion energy. RESULTS: Cardioversion was achieved in 71 of 72 episodes (62 of 63 horses) at a mean energy of 165.43 +/- 8.75 J. Cardioversion energy was higher for females than for males, and for interaction terms, weight was negatively related to energy in females and positively related in males. Age was positively related to cardioversion energy in females. No relationship was identified between duration of arrhythmia before treatment and prognosis for response or cardioversion energy. CONCLUSIONS AND CLINICAL IMPORTANCE: TVEC is highly effective in the treatment of lone AF in horses. Although age and sex influence cardioversion energy level, duration of arrhythmia does not.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".