ACVECC Veterinary Committee on Trauma Registry Report 2022–2023
Bibliographic record
Abstract
OBJECTIVE: To report summative data from the American College of Veterinary Emergency and Critical Care Veterinary Committee on Trauma (ACVECC-VetCOT) registry, with further summary reporting based on geographic region and human population density. DESIGN: Multi-institutional registry data report, January 1, 2022 to December 31, 2023. SETTING: Twenty-two Veterinary Trauma Centers (VTCs) identified and verified by ACVECC-VetCOT. ANIMALS: Dogs and cats with evidence of traumatic injury presented to contributing hospitals. PROCEDURES: Data were input into a web-based data capture system (Research Electronic Data Capture) by data entry personnel. Patient data on demographics, trauma type, preadmission care, trauma severity assessment at presentation, key laboratory parameters, interventions, and outcome were collected. Descriptive statistics were performed for each species reported. RESULTS: Twenty-two VTCs in North America and the United Kingdom contributed data to the VetCOT registry between January 1, 2022 and December 31, 2023. A total of 9,820 cases (8,130 dog, 1,690 cat) were reported. The top three causes of trauma in dogs were penetrating bite wounds (35% of all dog trauma cases), vehicular strikes (17%), and lacerations (12%); in cats, unknown blunt trauma (22% of all cat trauma cases), penetrating bite wounds (20%), and falls from heights (14%) were the leading causes of trauma. Prevalence of trauma types across geographic regions was similar except for porcupine quilling, which occurred primarily in the Northeast of the United States. Vehicular trauma and porcupine quilling occurred commonly in rural VTCs, whereas falls from heights and nonpenetrating bite wounds occurred commonly in urban VTCs. Survival to discharge remained high in both dogs and cats (93.1% and 83.7%, respectively). CLINICAL RELEVANCE: The ACVECC-VetCOT registry provides a foundation for retrospective evaluation of traumatic injury in dogs and cats. It has already contributed to the production of numerous publications assessing relationships between demographics, trauma etiology, and trauma severity with clinical outcomes.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.010 |
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".