A novel score based on age and cardiac biomarkers predicts outcomes in severe sepsis and septic shock
Bibliographic record
Abstract
Myocardial dysfunction is common among critically ill septic patients. Elevated levels of cardiac biomarkers are predictors of mortality in acute coronary syndrome and in unselected critically ill patients. Our aim was to evaluate the role of the cardiac markers NT-proBNP, Troponin T (TnT) and myoglobin as predictors of inhospital and 6-month all-cause mortality in patients admitted to a general adult ICU with severe sepsis/septic shock. Serial plasma samples were taken for five sequential days on all patients admitted with severe sepsis/septic shock. Samples were analysed for NT-proBNP, TnT and myoglobin. Samples were analysed on 49 patients. Elevated myoglobin was the only predictor of ICU mortality. Age, myoglobin and NT-proBNP levels predicted hospital mortality. Predictors of 6-month mortality were age, peak TnT, peak myoglobin and peak NT-proBNP levels. The APACHE II score did not predict mortality. A score was established dependent on TnT (<0.1 = 1, ≥0.1 = 2), age (<65 years = 1, ≥65 years = 2), BNP (<10,000 = 1, >10,000 = 2), and myoglobin (<750 = 1, >750 = 2). Patients were placed into tertiles (score = 4&5, 6, 7&8) to produce survival curves (Figure 1 , P < 0.01). (abstract P450) In critically ill patients with severe sepsis/septic shock a score based on age and increased plasma levels of cardiac biomarkers can help risk-stratify patients and predict short-term (<6 months) outcome.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".