ACVECC‐Veterinary Committee on Trauma registry report 2017–2019
Bibliographic record
Abstract
OBJECTIVE: To report summative data from the American College of Veterinary Emergency and Critical Care Veterinary Committee on Trauma (VetCOT) registry. DESIGN: Multi-institutional registry data report, April 1, 2017 to December 31, 2019. SETTING: VetCOT identified and verified Veterinary Trauma Centers (VTCs). ANIMALS: Dogs and cats with evidence of trauma. INTERVENTIONS: Data were input to a web-based data capture system (Research Electronic Data Capture) by data entry personnel trained in data software use and operational definitions of data variables. Data on demographics, trauma type, preadmission care, trauma severity assessment at presentation (modified Glasgow Coma Scale and Animal Trauma Triage score), key laboratory parameters, interventions, and case outcome were collected. Summary descriptive data for each species are reported. MEASUREMENTS AND MAIN RESULTS: Thirty-one VTCs contributed data from 20,842 canine and 4003 feline trauma cases during the 33-month reporting period. Most cases presented directly to a VTC (82.1% dogs, 82.1% cats). Admission to hospital rates were slightly lower in dogs (27.8%) than cats (32.7%). Highest mortality rates by mechanism of injury in dogs were struck by vehicle (18.3%), ballistic injury (17.6%), injured inside vehicle (13.2%), nonpenetrating bite wound (10.2%), and choking/pulling injury (8.5%). Highest mortality rates by mechanism of injury in cats were struck by vehicle (43.3%), ejected from vehicle (33.3%), nonpenetrating bite wound (30.7%), ballistic injury (27.8%), and choking/pulling injury (25.0%). The proportion of animals surviving to discharge was 93.1% (dogs) and 82.5% (cats). CONCLUSIONS: The VetCOT registry is a powerful resource for collection of a large dataset on trauma in dogs and cats seen at VTCs. Overall survival to discharge was high indicating low injury severity for most recorded cases. Further evaluation of data on subsets of injury types, patient assessment parameters, interventions, and associated outcome are warranted. Data from the registry can be leveraged to inform clinical trial design and justification for naturally occurring trauma as a translational model to improve veterinary and human trauma patient outcome.
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
| 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".