Citrate anticoagulation in a piglet model of pediatric continuous renal replacement therapy
Bibliographic record
Abstract
OBJECTIVE: To develop pediatric guidelines for the use of citrate as a regional anticoagulant for continuous renal replacement therapy (CRRT) using a neonatal piglet model. DESIGN: Prospective observational study. SETTING: Animal laboratory in the research center of a tertiary-level children's hospital. SUBJECTS: Ten neonatal piglets. INTERVENTIONS AND MEASUREMENTS: Using a venovenous CRRT circuit and filter, we randomly altered the filter blood flow rate, replacement flow rate, and citrate flow rate over conventional pediatric ranges. Measured end points were prefilter serum ionized calcium and citrate levels. MAIN RESULTS: A prefilter serum citrate concentration of 6 mmol/L is required to maintain the prefilter ionized calcium < or =0.4 mmol/L. Using multiple regression analysis on collected data, we derived a formula to predict prefilter serum citrate for combinations of replacement flow rate, blood flow rate, and citrate flow rate. CONCLUSIONS: The available literature and our past experience indicate that a prefilter ionized calcium < or =0.4 mmol/L is required to anticoagulate a CRRT circuit; a prefilter serum citrate concentration of 6 mmol/L is required to achieve this. Our multiple regression analysis can be expressed graphically to allow easy calculation of the required citrate flow rate, given the knowledge of the replacement flow rate and blood flow rate. Our results provide the first guidelines for the use of citrate as a regional anticoagulant in a pediatric-size model of CRRT.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".