Impact of malnutrition on survival in adult patients after elective cardiac surgery: Long-term follow up data
Bibliographic record
Abstract
The data article refers to the paper titles "Impact of malnutrition on long-term survival in adult patients after elective cardiac surgery" [1]. The data refer to the analysis of the relationship between baseline malnutrition and long-term mortality after cardiac surgery. Baseline demographic, nutritional, and medical history data were collected for each enrolled patient. Baseline serum albumin and C-reactive (CRP) protein levels were also obtained. Surgical risk was assessed in accordance with the logistic EuroSCORE. Intraoperative data including cardiopulmonary bypass (CPB) time and postoperative characteristics, such as postoperative complications, number of days in the ICU, and hospitalization duration, were also collected. Data on nutritional status were collected using four nutritional screening tools: (1) malnutrition universal screening tool (MUST), (2) short nutritional assessment questionnaire (SNAQ), (3) mini-nutritional assessment (MNA), and (4) nutritional risk screening 2002 (NRS-2002). Both electronic medical records and phone interviews were used for survival data collection. ROC analysis was performed to analyze prognostic value of baseline and perioperative variables on long-term mortality. Univariate and multivariate logistic regression analysis of predictors of 3- and 8-year mortality were performed. Kaplan-Meyer curves, describing the impact of baseline and perioperative characteristics on 3- and 8-year survival were also performed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".