Perioperative mortality rate and risk factors for death in dogs undergoing surgery for treatment of thoracic trauma: 157 cases (1990–2014)
Bibliographic record
Abstract
OBJECTIVE To determine perioperative mortality rate and identify risk factors associated with outcome in dogs with thoracic trauma that underwent surgical procedures and to evaluate the utility of the animal trauma triage (ATT) score in predicting outcome. DESIGN Retrospective case series. ANIMALS 157 client-owned dogs. PROCEDURES Medical records databases of 7 veterinary teaching hospitals were reviewed. Dogs were included if trauma to the thorax was documented and the patient underwent a surgical procedure. History, signalment, results of physical examination and preoperative laboratory tests, surgical procedure, perioperative complications, duration of hospital stay, and details of follow-up were recorded. Descriptive statistics and ATT scores were calculated, and logistic regression analysis was performed. RESULTS 123 of 157 (78%) patients underwent thoracic surgery, and 134 of 157 (85.4%) survived to discharge. Mean ± SD ATT score for nonsurvivors was 8 ± 2.4. In the multivariable model, female dogs and dogs that did not experience cardiac arrest as a postoperative complication had odds of survival 6 times and 102 times, respectively, those of male dogs and dogs that did experience cardiac arrest as a postoperative complication. Additionally, patients with a mean ATT score < 7 had odds of survival 5 times those of patients with an ATT score ≥ 7. CONCLUSIONS AND CLINICAL RELEVANCE The overall perioperative mortality rate was low for patients with thoracic trauma undergoing surgery in this study. However, male dogs and dogs that experienced cardiac arrest had a lower likelihood of survival to discharge. The ATT score may be a useful adjunct to assist clinical decision-making in veterinary patients with thoracic trauma.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".