Association between outcome and organ system dysfunction in dogs with sepsis: 114 cases (2003–2007)
Bibliographic record
Abstract
OBJECTIVE: To determine whether multiple organ dysfunction syndrome (MODS) could be identified in dogs with sepsis secondary to gastrointestinal tract leakage, and whether the number of affected organ systems was significantly associated with mortality rate. DESIGN: Multicenter retrospective case series. ANIMALS: 114 dogs. PROCEDURES: Medical records for dogs treated surgically because of sepsis secondary to gastrointestinal tract leakage between 2003 and 2007 were reviewed. Sepsis was diagnosed on the basis of results of bacterial culture of peritoneal fluid, gross evidence of gastrointestinal tract leakage at surgery, or both. Renal dysfunction was defined as a > or = 0.5 mg/dL increase in serum creatinine concentration after surgery. Cardiovascular dysfunction was defined as hypotension requiring vasopressor treatment. Respiratory dysfunction was defined as a need for supplemental oxygen administration or mechanical ventilation. Hepatic dysfunction was defined as a serum bilirubin concentration > 0.5 mg/dL. Dysfunction of coagulation was defined as prolonged prothrombin time, prolonged partial thromboplastin time, or platelet count < or = 100,000/microL. RESULTS: 89 (78%) dogs had dysfunction of 1 or more organ systems, and 57 (50%) dogs had MODS. Mortality rate increased as the number of dysfunctional organ systems increased. Mortality rate was 70% (40/57) for dogs with MODS and 25% (14/57) for dogs without. CONCLUSIONS AND CLINICAL RELEVANCE: Results indicated that MODS, defined as dysfunction of at least 2 organ systems, can be identified in dogs with sepsis and that organ system dysfunction increased the odds of death.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".