The development of ventricular fibrillation as a complication of pericardiectomy in 16 dogs
Bibliographic record
Abstract
OBJECTIVE: To describe the clinical characteristics, perioperative protocols, and outcomes in dogs diagnosed with ventricular fibrillation (VF) while undergoing pericardiectomy. STUDY DESIGN: Retrospective, multi-institutional study. ANIMALS: Sixteen client-owned dogs. METHODS: Cases were accrued through a listserve request posted to 3 subspecialty veterinary societies. Dogs were included if they developed VF during a pericardiectomy performed through an open or thoracoscopic approach. Data collected included signalment, history and physical examination, surgical approach, histopathology, treatment, and outcome. RESULTS: Indications for pericardiectomy included idiopathic chylothorax (n = 7), neoplasia (4), idiopathic pericardial effusion (4), and foreign body granuloma (1). Surgical approaches included thoracoscopy (12), intercostal thoracotomy (3) and median sternotomy (1). Electrosurgical devices were used to complete at least part of the pericardiectomy in 15 of 16 dogs. Ventricular fibrillation appeared to be initiated during electrosurgical use in 8/15 dogs. However, in 5/15 dogs it was not obviously associated with electrosurgical use. In 3/16 dogs the timing of initiation of VF was unclear. In 7/16 dogs, cardiac arrhythmias were noted prior to the development of VF. Fourteen of 16 dogs died from intraoperative VF. CONCLUSION: In most dogs ventricular fibrillation was a fatal complication of pericardiectomy. Ventricular fibrillation might be associated with the use of electrosurgical devices and cardiac manipulation during pericardiectomy although a causal link could not be established from the data in this study. CLINICAL SIGNIFICANCE: Surgeons must be aware of the risk of VF during pericardial surgery. Electrosurgery might need to be used judiciously during pericardiectomy, particularly in dogs exhibiting cardiac arrythmias.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".