RENAL OUTCOMES FOLLOWING HYDROXYETHYL STARCH RESUSCITATION: A META-ANALYSIS OF RANDOMISED TRIALS
Bibliographic record
Abstract
Objectives: To evaluate the impact of HES solutions on adverse renal outcomes and mortality in critically ill patients requiring acute volume resuscitation. Design: Systematic review and meta-analysis of randomized controlled trials. Data Sources: We searched electronic databases from 1950 to 2007 (MEDLINE, EMBASE, the Cochrane Central Registry of Controlled Trials, and the SCOPUS database). Conference proceedings and grey literature sources were also searched from 2002-2007. Review Methods: We included all randomised controlled trials of patients requiring acute volume resuscitation who received HES compared to an alternative resuscitation fluid. No restrictions were considered regarding language or publication type. Data were independently extracted in duplicate. Results:Of 2381 citations reviewed, we included 22 trials (n=1866) in the analysis. Patients receiving HES were more likely to receive renal replacement therapy [odds ratio [OR] 1.91 (95% confidence interval [CI] 1.22-2.99, I^210.5%)]. This was also true for patients with severe sepsis or septic shock [OR 1.82 (95% CI 1.27-2.62, I^20%)]. In high quality trials, multicentre trials, and in reports indicating adequate allocation concealment, there was a trend toward increased risk of death associated with HES. Other adverse events were not systematically evaluated and were poorly reported. Limitations: Considerable clinical and methodologic heterogeneity exists among these trials. Conclusions: The use of HES for volume resuscitation in critically ill patients is associated with increased use of renal replacement therapy and may result in increased mortality. We caution against the routine use of HES for volume resuscitation in critically ill patients.
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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 | 0.040 | 0.081 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.026 | 0.056 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".