Systematic Review of the Effects of High-Volume High-Flow (HVHF) in Pediatric Sepsis
Bibliographic record
Abstract
Background: Pediatric sepsis is a significant public health issue. This condition is exacerbated by the presence of excess serum creatinine and inflammatory cytokines that lead to deleterious effects upon the body. The current standard of care involves the use of continuous kidney replacement therapy to remove harmful cytokines until the body returns to homeostasis. In order to promote faster clearance and reduced stay in the ICU, high volume high flow has been posited as a potential new modality of choice. However there is a paucity of studies to fully elucidate its benefits. Methods: A literature search was done using PubMed/Medline and Embase. Keywords used while conducting the literature search were, “hemofiltration OR haemofiltration OR hemodiafiltration” AND “high-volume”. The literature was reviewed by two independent reviewers, who independently assessed the quality of randomized controlled trials by using the Cochrane risk of bias tool for RCTs And Newcastle Ottawa Scale (NOS) for assessing the quality of non-randomized controlled trials. Data was combined from studies with similar design. Results: The primary endpoint of all cause mortality was found to be reduced by 40% across all of the pooled studies. For secondary endpoints, significant reductions of serum creatinine were found after 24 and 48 hours of use compared to the current standard of care. Additionally, duration of ICU stay and treatment course was found to be significantly shorter in HVHF patients than the current standard of care. Finally the rate of adverse effects were analyzed and there was no difference in the proportion of patients developing hypokalemia, hyperkalemia, hypernatremia or hyponatremia. The proportion of patients developing hyperglycemia was higher in patients undergoing HVHF whereas the proportions of patients developing bleeding was significantly less in patients undergoing HVHF. One study reported a total number of adverse events between the two groups which were significantly lesser in patients undergoing HVHF. Conclusions: HVHF shows promise as a modality to treat pediatric patients with sepsis. In order to confirm the benefits of this modality, future studies need significantly more patients for analysis.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.010 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".