Impact of specialized renal technologists on optimizing delivery of continuous kidney replacement therapy in critical care areas a retrospective study
Bibliographic record
Abstract
BACKGROUND: Continuous renal replacement therapy (CKRT) is delivered to some of the most critically ill patients in hospitals. This therapy is expensive and requires coordination of multidisciplinary teams to ensure the prescribed dose is delivered. With increased demands on the critical care nursing staff and increased complexities of patients admitted to critical care units, we evaluated the role of specialized renal technologists in ensuring the prescribed dose is delivered. Therefore, the aim of this study is to investigate the impact of supporting intensive care unit nurses with specialized renal technologists on optimizing efficiency of CKRT sessions in the United Arab Emirates. METHODS: This is a retrospective study that compared critically ill patients on CKRT overseen by specialized renal technologists versus who are non-covered in the year 2021. RESULTS: A total of 331 sessions on 158 patients were included in the study. The mean filter life was longer in specialized renal technologists-covered patients compared to the non-covered group (66 vs. 59 h, p = 0.019). After adjustment by multiple regression analysis for risk factors (i.e., age, gender, mechanical ventilation, sepsis, mean arterial pressure, vasopressors, and SOFA) that may affect CKRT machines' filter life, presence of a specialized renal technologists resulted in significantly longer filter life (co-efficient 0.129; CI 95% 1.080, 11.970; p-value: 0.019). CONCLUSION: Our study suggests that specialized renal technologists play a vital role in prolonging CKRT machine's filter life span and optimizing CKRT machine's efficiency. Further research should focus on other potential benefits of having specialized renal technologists performing CKRT sessions, and to confirm the finding of this study. Additionally, a cost-benefit analysis could be conducted to determine the economic impact of having specialized teams performing CKRT.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".