Serial Evaluation of Abdominal Fluid and Serum Amino-terminal pro-C-type Natriuretic Peptide in Dogs with Septic Peritonitis
Bibliographic record
Abstract
BACKGROUND: Serum N-terminal pro-C-natriuretic peptide (NT-proCNP) has shown promise as a diagnostic biomarker for sepsis. Its sensitivity to detect dogs with septic peritonitis (SP) is reportedly low, perhaps attributable to the compartmentalization of NT-proCNP in the abdominal cavity. OBJECTIVES: To evaluate the use of an ELISA for the measurement of NT-proCNP in canine abdominal fluid and to describe the peri-operative pattern of abdominal fluid and serum NT-proCNP concentrations in dogs with SP. ANIMALS: Five client-owned dogs with nonseptic abdominal effusion of varying etiologies and 12 client-owned dogs with SP undergoing abdominal surgery and placement of a closed-suction abdominal drain (CSAD). Six dogs were included upon hospital admission; 6 were included the day after surgery. METHODS: Prospective pilot study. A commercially available ELISA kit was analytically validated for use on canine abdominal fluid. The NT-proCNP concentrations were measured in the abdominal fluid of control dogs, and in serum and abdominal fluid of dogs with SP from admission for CSAD removal. RESULTS: In dogs with SP, admission abdominal fluid NT-proCNP concentrations were lower than the concurrent serum concentrations (P = 0.031), and lower than control canine abdominal fluid concentrations (P = 0.015). Postoperatively, abdominal fluid NT-proCNP concentrations remained lower than serum concentrations (P < 0.050), except on day 4. CONCLUSIONS AND CLINICAL IMPORTANCE: The ELISA kit was able to measure NT-proCNP in canine abdominal fluid. In dogs with SP, low serum NT-proCNP concentrations cannot be explained by abdominal compartmentalization.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".