Identifying a target group for selenium supplementation in high-risk cardiac surgery: a secondary analysis of the SUSTAIN CSX trial
Bibliographic record
Abstract
BACKGROUND: Recent data from the randomized SUSTAIN CSX trial could not confirm clinical benefits from perioperative selenium treatment in high-risk cardiac surgery patients. Underlying reasons may involve inadequate biosynthesis of glutathione peroxidase (GPx3), which is a key mediator of selenium's antioxidant effects. This secondary analysis aimed to identify patients with an increase in GPx3 activity following selenium treatment. We hypothesize that these responders might benefit from perioperative selenium treatment. METHODS: Patients were selected based on the availability of selenium biomarker information. Four subgroups were defined according to the patient's baseline status, including those with normal kidney function, reduced kidney function, selenium deficiency, and submaximal GPx3 activity. RESULTS: Two hundred and forty-four patients were included in this analysis. Overall, higher serum concentrations of selenium, selenoprotein P (SELENOP) and GPx3 were correlated with less organ injury. GPx3 activity at baseline was predictive of 6-month survival (AUC 0.73; p = 0.03). While selenium treatment elevated serum selenium and SELENOP concentrations but not GPx3 activity in the full patient cohort, subgroup analyses revealed that GPx3 activity increased in patients with reduced kidney function, selenium deficiency and low to moderate GPx3 activity. Clinical outcomes did not vary between selenium treatment and placebo in any of these subgroups, though the study was not powered to conclusively detect differences in outcomes. CONCLUSIONS: The identification of GPx3 responders encourages further refined investigations into the treatment effects of selenium in high-risk cardiac surgery 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".