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Record W29914158 · doi:10.3390/nu10060779

Plano de marketing o cartão de crédito no segmento private

2011· article· en· W29914158 on OpenAlexfundno aff
Rosane Agustini

Bibliographic record

VenueNutrients · 2011
Typearticle
Languageen
FieldDecision Sciences
TopicBusiness and Management Studies
Canadian institutionsnot available
FundersCanadian Institutes of Health Research
KeywordsCartBusinessEconomicsGeography

Abstract

fetched live from OpenAlex

Metabolic products of the intestinal microbiome such as trimethylamine N-oxide (TMAO) that accumulate in renal failure (gut-derived uremic toxins, GDUTs) affect atherosclerosis and increase cardiovascular risk. We hypothesized that patients on a Mediterranean diet and those consuming lower amounts of dietary precursors would have lower levels of GDUTs. Patients attending vascular prevention clinics completed a Harvard Food Frequency Questionnaire (FFQ) and had plasma levels of TMAO, p-cresylsulfate, hippuric acid, indoxyl sulfate, p-cresyl glucuronide, phenyl acetyl glutamine, and phenyl sulfate measured by ultra-performance liquid chromatography coupled to quadrupole time-of-flight mass spectrometry. Carotid plaque burden was measured by ultrasound; CKD-Epi equations were used to estimate the glomerular filtration rate. In total, 276 patients completed the study. Even moderate renal function significantly increased plasma GDUTs, which were significantly associated with higher carotid plaque burden. There was no significant difference in plasma levels of any GDUT associated with a Mediterranean diet score or with intake of dietary precursors. In omnivorous patients with vascular disease, the intake of dietary precursors of intestinal metabolites or adherence to a Mediterranean diet did not change plasma GDUT. Approaches other than diet, such as probiotics and repopulation of the intestinal microbiome, may be required to mitigate the adverse effects of GDUTs.

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)0.002
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 score0.215
Threshold uncertainty score0.934

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.122
GPT teacher head0.334
Teacher spread0.212 · 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

Citations0
Published2011
Admission routes1
Has abstractyes

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