Fluid resuscitation in severe sepsis and septic shock: systematic description of fluids used in randomized trials
Bibliographic record
Abstract
INTRODUCTION: Fluid therapy is one of the cornerstones of initial management of sepsis, but the choice of fluids used for resuscitation is controversial. OBJECTIVES: While trying to determine the effects of alternative fluids used in sepsis resuscitation randomized controlled trials (RCTs), we found that the precise description of those fluids was frequently not available. This report presents the result of our efforts to provide the characteristics of those fluids to both researchers and clinicians. METHODS: We searched the following electronic databases: CENTRAL, MEDLINE, EMBASE, CINAHL, and ACPJC, and examined the reference lists of recently published meta‑analyses of fluid therapies in critically ill patients. These databases were searched from inception until August 2013. The data abstraction stage included determination of fluid composition, pH, chloride concentration, and presence or absence of buffers. We relied on the original articles as well as on manufacturers' websites, contact with authors, and contact with experts in the field. RESULTS: Our original search yielded 7002 articles. In consecutive stages, we reduced it to 20. The types of fluids varied widely, including chloride content (110-154 mmol/l) and presence or absence of buffering substances in colloid solutions. Those characteristics were frequently not presented and rarely emphasized in the original articles. CONCLUSIONS: The basic characteristics of fluids used in fluid therapy trials are often not easily available, yet of increasingly recognized clinical importance. We provide the information concerning composition of fluids used in RCTs, which will be useful not only to future investigators and systematic reviewers but also to clinicians using those fluids in regular clinical practice.
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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 | 0.057 | 0.209 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.011 |
| Bibliometrics | 0.018 | 0.022 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".