Multicenter prospective evaluation of dogs with trauma
Bibliographic record
Abstract
OBJECTIVE: To determine hospital admission variables for dogs with trauma including values determined with scoring systems (animal trauma triage [ATT], modified Glasgow coma scale [MGCS], and acute patient physiologic and laboratory evaluation [APPLE] scores) and the usefulness of such variables for the prediction of outcome (death vs survival to hospital discharge). DESIGN: Prospective, multicenter, cohort study. ANIMALS: 315 client-owned dogs. PROCEDURES: By use of a Web-based data capture system, trained personnel prospectively recorded admission ATT, MGCS, and APPLE scores; clinical and laboratory data; and outcome (death vs survival to discharge) for dogs with trauma at 4 veterinary teaching hospitals during an 8-week period. RESULTS: Cause of injury was most commonly blunt trauma (173/315 [54.9%]) followed by penetrating trauma (107/315 [34.0%]), or was unknown (35/315 [11.1%]). Of the 315 dogs, 285 (90.5%) survived to hospital discharge. When 16 dogs euthanized because of cost were excluded, dogs with blunt trauma were more likely to survive, compared with dogs with penetrating trauma (OR, 8.5). The ATT (OR, 2.0) and MGCS (OR, 0.47) scores and blood lactate concentration (OR, 1.5) at the time of hospital admission were predictive of outcome. Surgical procedures were performed for 157 (49.8%) dogs; surgery was associated with survival to discharge (OR, 7.1). CONCLUSIONS AND CLINICAL RELEVANCE: Results indicated ATT and MGCS scores were useful for prediction of outcome for dogs evaluated because of trauma. Penetrating trauma, low blood lactate concentration, and performance of surgical procedures were predictive of survival to hospital discharge. The methods enabled collection of data for a large number of dogs in a short time.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".