ACVECC‐Veterinary Committee on Trauma registry report 2020–2021
Bibliographic record
Abstract
OBJECTIVE: To report summative data from the American College of Veterinary Emergency and Critical Care (ACVECC)-Veterinary Committee on Trauma (VetCOT) registry, with further individual evaluation of university and private practices and level I and II Veterinary Trauma Centers (VTCs). DESIGN: Multi-institutional registry data report, January 1, 2020, to December 31, 2021. SETTING: VTCs identified and verified by ACVECC-VetCOT. ANIMALS: Dogs and cats with evidence of trauma. INTERVENTIONS: Data were input to a web-based data capture system (REDCap) by data entry personnel trained in registry software use and operational definitions of data variables. Patient data on demographics, trauma type, preadmission care, trauma severity assessment at presentation (modified Glasgow Coma Score and Animal Trauma Triage score), key laboratory parameters, interventions, and outcome were collected. Summary descriptive data for each species are reported. MEASUREMENTS AND MAIN RESULTS: Twenty-two VTCs contributed data to the VetCOT registry during a 24-month period, culminating in a total of 9758 complete trauma case records for dogs and 11734 for cats. Head trauma in dogs and cats was seen at a higher percentage in both university-only VTCs (encompassing both level I and II) (20.1% and 24.1%, respectively) and level I-only VTCs (24.3% and 24.1%, respectively), in comparison to private-only VTCs (encompassing both level I and II) (13.5% and 16.2%, respectively) and individual level II VTCs (14.1% and 18.9%, respectively). Canine and feline surgical procedures were performed at a higher percentage at university VTCs (50% and 40.5%, respectively) compared to private VTCs (39.2% and 28.6%, respectively). Overall survival to discharge for dogs and cats remains high at 93.1% and 83.6%, respectively. CONCLUSIONS: The VetCOT registry has continued to show powerful potential in collating a large, multifaceted, international dataset in trauma for both dogs and cats. As published in previous VetCOT registry reports, survival to discharge has remained static across both university and private practice veterinary hospitals; however, further breakdown has identified university and level I VTCs admitting and managing a higher number of head traumas, as well as university VTCs performing a higher proportion of surgical procedures. Data from this registry will continue to aid in the design of clinical trials, prospective observational studies, and translational research, which will improve the understanding and outcome of trauma patients.
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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 | 0.019 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".