Fluid management and deresuscitation practices: A survey of critical care physicians
Bibliographic record
Abstract
Accumulation of a positive fluid balance is common in critically ill patients, and is associated with adverse outcomes, including mortality. However, there are few randomised clinical trials to guide clinicians as to the most appropriate fluid strategy following initial resuscitation and on the use of deresuscitation (removal of accumulated fluid using diuretics and/or renal replacement therapy). To inform the design of randomised trials, we surveyed critical care physicians with regard to perceptions of fluid overload in critical care, self-reported practice, acceptability of a variety of approaches to deresuscitation, appropriate safety parameters, and overall acceptability of a randomised trial of deresuscitation. Of 524 critical care specialists completing the survey, the majority practiced in mixed medical/surgical intensive care units in the United Kingdom. Most (309 of 363 respondents, 85%) believed fluid overload to be a modifiable source of morbidity; there was strong support (395 of 457, 86%) for a randomised trial of deresuscitation in critical illness. Marked practice variability was evident among respondents. In a given clinical scenario, self-reported practice ranged from the administration of fluid (N = 59, 14%) to the administration of a diuretic (N = 285, 67%). The majority (95%) considered it appropriate to administer diuretics for fluid overload in the setting of noradrenaline infusion and to continue to administer diuretics despite mild dysnatraemias, hypotension, metabolic alkalosis, and hypokalaemia. The majority of critical care physicians view fluid overload as a common and modifiable source of morbidity; deresuscitation is widely practiced, and there is widespread support for randomised trials of deresuscitation in critical illness.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".