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Record W3217632808 · doi:10.3390/nu13124235

Association between Diet and Xerostomia: Is Xerostomia a Barrier to a Healthy Eating Pattern?

2021· article· en· W3217632808 on OpenAlexaff
Indrė Stankevičienė, Jolanta Aleksejūnienė, Алина Пуриене, Lina Stangvaltaite‐Mouhat

Bibliographic record

VenueNutrients · 2021
Typearticle
Languageen
FieldMedicine
TopicSalivary Gland Disorders and Functions
Canadian institutionsUniversity of British Columbia
FundersBorrow FoundationUniversitetet i Tromsø
KeywordsLogistic regressionMedicineRed meatFood scienceInternal medicineBiology

Abstract

fetched live from OpenAlex

Objective. Xerostomia is a subjective feeling of dry mouth and is commonly observed in patients with autoimmune diseases. Our study examines the association between xerostomia and diet. Materials and Methods. The cross-sectional study includes 1405 adults from 15 Lithuanian geographical areas (52% response rate). A self-reported questionnaire inquired about xerostomia, sex, age, education, residence, and consumption of selected 23 diet items. For the multivariable analysis, 23 diet items were categorized into eight major diet groups. The data were analyzed by bivariate and multivariable analyses. Results. When comparing participants with and without xerostomia, there were significant differences in consumption frequencies concerning cold-pressed oil (p = 0.013), bread (p = 0.029), processed meat products (p = 0.016), fat and lean fish (p = 0.009), and probiotic supplements (p = 0.002). In the multivariable binary logistic regression model, when controlled for other determinants, the higher consumption of carbohydrates (OR 0.39, 95% CI 0.23–0.65), proteins (OR 0.56, 95% CI 0.32–0.99), and oils (OR 0.58, 95% CI 0.34–1.00) was associated with a lower likelihood of xerostomia. Conclusions. The association between xerostomia and the consumption of the six diet items—cold-pressed oils, lean and fat fish, bread, processed meat, and probiotic supplements— and the three major diet groups—carbohydrates, proteins, and oils—was observed. Longitudinal studies are needed to validate the observed associations.

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 score0.016
Threshold uncertainty score0.432

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 head0.018
GPT teacher head0.281
Teacher spread0.264 · 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

Citations14
Published2021
Admission routes1
Has abstractyes

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