Serum procalcitonin concentrations in dogs with induced endotoxemia
Bibliographic record
Abstract
BACKGROUND: Procalcitonin (PCT) is an important biomarker for sepsis in human medicine, but there is little information regarding PCT as a biomarker for sepsis in dogs. There are no controlled studies evaluating serial concentrations of PCT in dogs. HYPOTHESIS/OBJECTIVE: That PCT would be rapidly detectable in serum after injection of LPS and would remain increased for at least 24 hours. Objective was to evaluate serial serum PCT concentrations in dogs after a single IV injection of LPS compared to placebo. ANIMALS: Six healthy mixed breed dogs. METHODS: A nonrandomized, placebo-controlled, crossover study was performed. Dogs were initially injected with placebo (0.9% NaCl; 1 mL, IV) and then experimental endotoxemia was induced by injecting lipopolysaccharide (LPS; 2 μg/kg, IV, once) after a 5-day washout period. Serial blood samples were collected for measurement of serum PCT after each injection. Difference in median PCT concentration between serial time points was assessed using a mixed effects model. RESULTS: After LPS administration, blood pressure decreased and body temperature increased along with the development of lethargy, vomiting, and diarrhea. Procalcitonin was significantly increased compared to baseline by 2 hours after injection of LPS (median = 67.9 versus 172.8, range = 46.0-74.1 versus 99.5-295.9, P = .0002) and remained significantly increased for 12 hours (median = 205.9, range = 119.9-297.4) with return to baseline by 48 hours. Procalcitonin was significantly higher than placebo 2, 4, 6, 8, 10, 12, and 24 hours after injection. There were no significant differences in PCT between time 0 and any of the subsequent time points in the saline group. CONCLUSIONS AND CLINICAL IMPORTANCE: Procalcitonin expression is likely to be a clinically useful biomarker for sepsis in dogs and might have an additional role in prognostication and therapeutic decision-making.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".