Markers of tissue perfusion and their relation to mortality in dogs with blunt trauma
Bibliographic record
Abstract
OBJECTIVE: To evaluate admission Animal Trauma Triage (ATT) score, shock index (SI), and markers of perfusion, including base excess (BE), plasma lactate, and lactate clearance in dogs with blunt trauma. DESIGN: Prospective observational clinical study from 2013 to 2015. SETTING: Private veterinary referral and emergency center. ANIMALS: Forty-four client-owned dogs hospitalized following blunt trauma. INTERVENTION: Within 1 hour of presentation and prior to fluid administration an initial hematocrit, total plasma protein, blood glucose, plasma lactate, blood gas, and electrolytes were obtained for analysis. Plasma lactate concentrations were also measured 4 and 8 hours following initial measurement, and a 4-hour lactate clearance was calculated if patients had an increased admission plasma lactate. ATT score and SI were calculated for each patient based on admission data. Outcome was defined as survival to hospital discharge. MEASUREMENTS AND MAIN RESULTS: (15.7 vs 18.8 mmol/L, P = 0.004), lower median admission BE (-11.0 vs -7.0 mmol/L, P = 0.004), and higher median admission lactate (3.1 vs 2.4 mmol/L, P = 0.036) than those who survived. Median ATT was significantly higher in nonsurvivors (5 vsF 2, P < 0.001). The SI was not significantly different between survivors and nonsurvivors (P = 0.41). There was no difference in median 4-hour lactate (P = 0.34), median 8-hour lactate (P = 0.19), or 4-hour lactate clearance (P = 0.83) in survivors compared to nonsurvivors. No other statistically significant differences were noted between groups. CONCLUSION: , and BE and a higher admission plasma lactate were less likely to survive to hospital discharge. Median ATT score was also significantly higher in nonsurvivors. Although lactate clearance was not predictive of survival, the sample size was small, and additional studies with a larger study population are warranted.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".