Short-Term Reactions to Oliguria in Critically Ill Patients: A Retrospective Cohort Study
Bibliographic record
Abstract
Background/Objective: Oliguria is common in critically ill patients and may indicate impaired kidney perfusion or acute injury, contributing to increased mortality. Effective management is essential to improve outcomes. To assess clinician reactions to oliguria and evaluate the effectiveness of fluid bolus and furosemide interventions. Methods: A retrospective cohort study was conducted using ICU data from a single center (2017–2023). Oliguria was defined as two consecutive hours of urine output < 20 mL/h following at least three hours > 20 mL/h. Clinicians’ reactions within four hours were categorized as no intervention, fluid bolus (>250 mL), or furosemide administration. Outcomes included urine output, fluid balance, and serum creatinine. Results: Among 4987 oliguria episodes, 4007 events in 1825 patients were analyzed: no reaction (2536), fluid bolus (923), and furosemide (548). Furosemide significantly increased urine output (53.9 to 75.3 mL/h, p < 0.001), while fluid bolus had no significant effect. Resolution of oliguria (mean urine output > 40 mL/h for 5 h post-intervention) was more frequent with furosemide (66.4%) than with fluid bolus (28.4%) or no reaction (27.6%) (p < 0.001). Treatment choices varied significantly among ICU attendings (p < 0.001). Conclusions: Furosemide was more effective than fluid bolus or no treatment in improving urine output and resolving oliguria. The observed variation in clinician practices underscores the need for standardized management protocols to enhance patient care.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".