Fluid Overload and Risk of Mortality in Critically Ill Patients
Bibliographic record
Abstract
BACKGROUND: Fluid overload (FO) is a condition present in critical care units, and it is associated with clinical complications and worse outcomes for severe patients. OBJECTIVE: The aim of this study was to verify if FO is a risk factor for mortality in critically ill patients. METHODS: Retrospective study performed in a Brazilian intensive care unit, from January to March 2016, with patients older than 18 years and hospitalized for more than 24 hours. Demographic and clinical data, as well as fluid balance and overload, were analyzed to verify the risk factors for mortality. A logistic regression model was elaborated, and significance was set at P < .05. RESULTS: There were 158 patients included, of which only 13 (8.2%) presented FO. Mortality was verified in individuals 30 (18.9%), of whom only 7 (23.3%) developed FO, which was lower in survivors 6 (4.9%), P = .001. In the simple regression model, the FO was significant (odds ratio [OR], 6.23; 95% confidence interval [CI], 2.04-19.53), P = .001. However, in the multiple regression model, there were significant findings only for mechanical ventilation (OR, 5.86; 95% CI, 2.10-18.12, P = .001), acute kidney injury (OR, 4.05; 95% CI, 1.53-11; P = .001), and noradrenaline (OR, 3.85; 95% CI, 1.01-9.51; P = .041); FO was not significant (OR, 3.68; 95% CI, 0.91-15.55; P = .069). CONCLUSION: Fluid overload is higher in patients who died. Therefore, it was not considered a risk factor for mortality.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".