Ultrafiltration in Pediatric Cardiac Surgery Review
Bibliographic record
Abstract
INTRODUCTION: The use of cardiopulmonary bypass in pediatric cardiac surgery is associated with significant inflammation, fluid overload, and end-organ dysfunction yielding morbidity and mortality. For decades, various intraoperative ultrafiltration techniques such as conventional ultrafiltration, modified ultrafiltration (MUF), zero-balance ultrafiltration (ZBUF), and combination techniques (ZBUF-MUF) have been used to mitigate these toxicities and promote improved postoperative outcomes. However, there is currently no consensus on the ultrafiltration technique or strategy that yields the most benefit for infants and children undergoing open heart surgery. METHODS: A librarian-conducted PubMed literature search from 1990 to 2018 yielded 90 clinical studies or publications on the various forms of ultrafiltration and the impact on physiologic markers and clinical outcomes. All publications were reviewed, summarized, and conclusions synthesized. The data sets were not combined for systematic or meta-analysis due to significant heterogeneity in study protocols and patient populations. RESULTS: Modified ultrafiltration significantly promotes improved myocardial function, reduction in fluid overload, and reduced bleeding and transfusion complications. Furthermore, ZBUF has shown a consistent reduction in inflammatory cytokines and improved pulmonary function and compliance. There is conflicting evidence that MUF, ZBUF, and ZBUF-MUF culminate in reduced ventilation time and intensive care unit stay. CONCLUSION: Various modes of ultrafiltration have been shown to be associated with improved physiologic function or clinical outcomes in pediatric cardiac surgery. There are some inconsistent trial results that can be explained by heterogeneity in ultrafiltration, clinical staff preferences, and institution protocols. Ultrafiltration has some essential benefit as it is ubiquitously used at pediatric heart centers; however, the optimal protocol could be yet identified.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".