Bibliographic record
Abstract
Background and aims: Acute kidney injury is a common occurrence in critically ill children, frequently seen as a result of complications from other disease treatments or processes. Children who require intensive care as a result of sepsis, cardiopulmonary bypass, acute respiratory distress syndrome or inborn errors of metabolism, may develop acute kidney injury requiring prompt intervention to prevent further deterioration. With its gradual removal of fluids and toxins minimizing the hemodynamic instability seen in more rapid methods of fluid removal, continuous renal replacement therapy (CRRT) is often considered the treatment of choice. Aims: This presentation will include a case-based approach to the nursing care of critically ill children requiring CRRT. Scenarios will review the nursing care needs of emergent, life-threatening situations as well as conditions requiring a prolonged course. Methods: Recent developments in equipment for use in lower body weight patients have addressed concerns of adapting equipment designed and tested in adults for pediatric use. Morbidity and mortality remain high, and complications of therapy are frequent. Complex case scenarios in paediatric CRRT therapy will be reviewed. Results: Although treatment plans are initiated and guided by physicians, the addition of a highly invasive therapy to the nursing care of children requires a solid understanding of the critically ill child, continuous renal replacement therapy and the potential for complications. Conclusions: Expert nurse CRRT clinicians anticipate, monitor, assess and intervene appropriately thus positively impacting patient outcomes and minimizing complications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.559 | 0.413 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".