Proatrial natriuretic peptide is a better predictor of 28-day mortality in septic shock patients than proendothelin-1
Bibliographic record
Abstract
BACKGROUND: Septic shock is a major health care problem that affects a heterogeneous population of patients. To improve sepsis management, a key point is to decrease this heterogeneity by stratifying patients according to specific criteria, such as appropriate biomarkers. As the early phase of septic shock is characterized by cardiovascular dysfunction, precursors of vasoactive hormones represent interesting candidates. The objective of the present study was to concomitantly assess the predictive value of C-terminal proendothelin-1 and midregional proatrial natriuretic peptide (CT-proET-1 and MR-proANP, respectively vasoconstrictor and vasodilator) on 28-day mortality following septic shock. METHODS: In this observational study which included 99 patients, concentrations of MR-proANP and CT-proET-1 were measured using an immunoluminometric assay three times within the first week after the onset of septic shock. RESULTS: While MR-proANP concentrations were significantly increased in non-survivors in comparison with survivors, no differences were noted for CT-proET-1. Increased MR-proANP concentrations were significantly associated with mortality after both univariate and multivariate analyses, adjusted for usual clinical confounders [SAPS II (simplified acute physiology score II), SOFA (sepsis-related organ failure assessment) scores and number of co-morbidities]. CONCLUSIONS: In septic shock patients, MR-proANP appears to be a good predictor of 28-day mortality, whereas CT-proET-1 does not present any predictive value during monitoring.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".