Frusemide Administration in Critically Ill Patients by Continuous Compared to Bolus Therapy
Bibliographic record
Abstract
BACKGROUND: Frusemide is frequently administered to critically ill patients in the intensive care unit (ICU). We investigated whether continuous frusemide infusion therapy was more effective than regular intermittent bolus doses at causing diuresis. METHODS: 59 adult patients with fluid overload admitted to two tertiary multidisciplinary ICUs were randomised to either a continuous frusemide infusion or regular intermittent intravenous boluses of frusemide according to pre-defined algorithms aiming for a minimum hourly urine output. RESULTS: There was no significant difference in diuretic response between the two groups during the first 24 h (5.3 liters in the bolus group vs. 5.4 liters in the infusion group). In the bolus group a significantly higher dose of frusemide was needed to achieve target diuresis (24.1 vs. 9.2 mg/h in the infusion group, p = 0.0002). Mean urine output per dose of frusemide was significantly higher in the infusion group (31.6 vs. 18 ml/mg in the bolus group, p = 0.014). At the end of the study, there were no differences in hospital mortality, number of patients requiring ventilatory support, change in serum creatinine or change in estimated glomerular filtration rate. CONCLUSIONS: Both intermittent boluses and continuous infusion of frusemide were successful in achieving algorithm-driven diuresis. However, continuous infusion therapy was more effective than intermittent boluses since the dose of frusemide required was significantly less.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".