Acute kidney injury in cardiac surgery patients receiving hydroxyethyl starch solutions
Bibliographic record
Abstract
In a retrospective study by a Canadian team [1], pentastarch infusion was a dose-related independent risk factor for acute kidney injury (AKI) after cardiac surgery. In a new retrospective cardiac surgery study by that team [2], 83% of patients received hydroxyethyl starch (HES) 130/0.4. For unexplained reasons, 25 to 43% of patients received both HES 130/0.4 and pentastarch. The team 'hypothesized that both synthetic starches and albumin-containing solutions are independently associated with AKI following cardiac surgery in a dose-dependent fashion'. However, they focused on albumin and never thoroughly evaluated HES-related AKI. Although univariate analyses were reported, propensity matching according to either HES 130/0.4 or pentastarch administration was omitted. Systematic allocation of low-risk patients to HES could have masked an association with AKI in the univariate analyses. Consequently, the study is misleading, since it suggests that albumin is associated with AKI while HES is not. We described a prospective study in 6,478 consecutive cardiac surgery patients [3]. With propensity matching, predominant use of HES 130/0.4 was associated with increased utilization of renal replacement therapy: odds ratio 1.46 and 95% confidence interval (CI) 1.08 to 1.97. Furthermore, in a meta-analysis of 15 randomized trials evaluating perioperative HES administration, including five in cardiac surgery, renal replacement therapy was increased by HES solutions as a class with relative risk 1.44 and CI 1.04 to 2.01 and by HES 130/0.4 in particular (relative risk 1.47, CI 1.02 to 2.12) [4]. Based on these results and other currently available data, complete avoidance of HES solutions such as HES 130/0.4 has been recommended [5].
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".