Prognostic Value and Development of a Scoring System in Horses With Systemic Inflammatory Response Syndrome
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Despite its widespread use in equine medicine, the clinical value of the systemic inflammatory response syndrome (SIRS) concept in horses remains unknown. OBJECTIVES: To study the prognostic value of measures of SIRS in horses and identify the best model of severe SIRS to predict outcome. ANIMALS: A total of 479 consecutive adult horse emergency admissions to a private primary referral practice. METHODS: Prospective observational study. All adult horses admitted for emergency treatment over the study period were included. Multivariate logistic regression and stepwise model selection were used. RESULTS: Each of the 4 SIRS criteria was associated with outcome in this population. Thirty-one percent of emergency cases had 2 or more abnormal SIRS criteria on admission and were defined as SIRS cases. SIRS was associated with increased odds of death (odds ratio [OR] = 8.22; 95% CI, 4.61-15.18; P < .001), an effect mainly found for acute gastrointestinal cases. SIRS cases were assigned a SIRS score of 2, 3, or 4, according to the number of abnormal SIRS criteria fulfilled on admission, and SIRS3 and SIRS4 cases had increased odds of death compared to SIRS2 cases (OR = 4.45; 95% CI, 1.78-11.15; P = .002). A model of severe SIRS including the SIRS score, blood lactate concentration, and color of the mucous membranes best predicted outcome in this population of horses. CONCLUSIONS AND CLINICAL IMPORTANCE: Systemic inflammatory response syndrome is associated with an increased risk of death in adult horses presenting with acute gastrointestinal illnesses. The model of severe SIRS proposed in this study could be used to assess the status and prognosis of adult equine emergency admissions.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 it