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Record W3112251172 · doi:10.1016/j.dib.2020.106651

Impact of malnutrition on survival in adult patients after elective cardiac surgery: Long-term follow up data

2020· article· en· W3112251172 on OpenAlexaff
Sergey Efremov, Tatyana Ionova, Tatiana Nikitina, Pavel E. Vedernikov, Timur A. Dzhumatov, Timofey Ovchinnikov, Abduvahhob A. Rashidov, Alexandr E Khomenko, Christian Stoppe, Daren K. Heyland, В. В. Ломиворотов

Bibliographic record

VenueData in Brief · 2020
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsQueen's University
Fundersnot available
KeywordsMedicineMalnutritionPerioperativeLogistic regressionUnivariate analysisMultivariate analysisMedical recordCardiac surgerySurgeryInternal medicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.551

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.096
GPT teacher head0.370
Teacher spread0.274 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2020
Admission routes1
Has abstractyes

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