Bibliographic record
Abstract
PURPOSE OF REVIEW: Fluid (volume) therapy is an integral component in the management of critically ill patients and fluid management may influence outcome. There is much controversy, however, about the type, timing and amount of fluid therapy. Here, we discuss the evidence available to guide such choices. RECENT FINDINGS: Fluid therapy is widely endorsed for resuscitation of critically ill patients across a range of conditions. Yet, the approach to fluid therapy is subject to substantial variation in clinical practice. Emerging data show that the choice, timing and amount of fluid therapy may affect clinical outcomes. Synthetic colloids may increase the risk of acute kidney injury. Albumin may benefit hypoalbuminemic patients with sepsis and acute lung injury but may worsen outcome in traumatic brain injury. Early administration of fluid therapy in sepsis may improve survival but may be unnecessary in patients with penetrating trauma. Later fluid therapy in acute lung injury patients will increase the duration of ventilator dependence without achieving better survival. A positive cumulative balance likely contributes to increased morbidity and mortality after major surgery. SUMMARY: Emerging evidence shows that choice, timing and amount of fluid therapy affect outcome. Future studies need to focus on these aspects of fluid therapy by means of larger, more rigorous and blinded controlled trials.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".