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Record W2029983646 · doi:10.1080/01635581.2011.587228

Preoperative Glucose and Protein Metabolism: The Influence of Diabetes Mellitus Type 2 in Patients With Colorectal Tumors

2011· article· en· W2029983646 on OpenAlexafffund
Andrea Kopp Lugli, Francesco Donatelli, Thomas Schricker, Linda Wykes, Franco Carli

Bibliographic record

VenueNutrition and Cancer · 2011
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsMcGill University
FundersCanadian Institutes of Health Research
KeywordsInternal medicineEndocrinologyMedicineHypermetabolismCarbohydrate metabolismMetabolismGluconeogenesisDiabetes mellitusProtein turnoverProtein metabolismColorectal cancerType 2 Diabetes MellitusType 2 diabetesBiologyCancerBiochemistryProtein biosynthesis

Abstract

fetched live from OpenAlex

Hypermetabolism, abnormal plasma amino acid profiles, increased gluconeogenesis, and changes in liver and muscle protein turnover are well-described undesirable effects in patients with cancer and diabetes mellitus type 2 (DM2) The aim of the present study was to analyze the specific impact and interaction of these 2 disease patterns on patients' preoperative glucose and protein metabolism. Eight nondiabetic and 8 diabetic patients devoid of cachexia underwent a stable isotope infusion study on the day before surgery for colorectal cancer or adenoma with high-grade dysplasia. Protein and glucose kinetics were assessed in a fasted state by L-[1-(13)C]leucine and [6,6(2)H(2)]glucose. In diabetic patients, glucose metabolism was found to be elevated as the plasma glucose level increased (P = 0.013) and endogenous rate of appearance of glucose tended to be higher compared to nondiabetic patients (P = 0.083). Protein metabolism was not affected by the metabolic state of the 2 groups. Resting energy expenditure was higher in diabetic patients (P = 0.028). Under postabsorptive conditions, noncachectic patients with DM2 suffering from colorectal tumors showed an elevated turnover in glucose metabolism whereas the nondiabetic counterparts failed to demonstrate any metabolic changes due solely to malignancy.

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)0.000
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 score0.010
Threshold uncertainty score0.145

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.016
GPT teacher head0.263
Teacher spread0.248 · 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

Citations2
Published2011
Admission routes2
Has abstractyes

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