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Record W2277622783 · doi:10.6133/apjcn.2016.25.2.12

Mediterranean diet adherence and risk of multiple sclerosis: a case-control study.

2016· article· en· W2277622783 on OpenAlexaff
Fatemeh Sedaghat, Mahsa Jessri, Maryam Behrooz, Mostafa Mirghotbi, Bahram Rashidkhani

Bibliographic record

VenuePubMed · 2016
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineOdds ratioConfidence intervalLogistic regressionMediterranean dietInternal medicineMultivariate analysisMultivariate statisticsMultiple sclerosisDemographyImmunology

Abstract

fetched live from OpenAlex

BACKGROUND AND OBJECTIVES: We conducted a hospital-based, case-control study to examine the association between Mediterranean diet (MD) and the risk of multiple sclerosis (MS) in Iran. METHODS AND STUDY DESIGN: A total of 70 patients with MS and 142 controls underwent face-to-face interviews in the major neurological clinics of Tehran, Iran. Adherence to a MD was assessed using the 9-unit dietary score, to evaluate the level of conformity of the individual's diet to the Mediterranean dietary pattern. Multivariate logistic regression was used to estimate odds ratios (OR) and 95% confidence intervals (CI). RESULTS: Higher consumption of fruits (OR=0.28, 95% CI: 0.12-0.63, p-value: 0.002) and vegetables (OR=0.23, 95% CI: 0.10-0.53, p-value: 0.001) were significantly associated with reduced MS risk. In both age adjusted and multivariate adjusted model, the OR of MS decreased significantly in the third as compared to the first tertile of MD score (age adjusted OR: 0.21, 95% CI: 0.06-0.67; p-trend: 0.01, Multivariate adjusted OR: 0.23, 95% CI: 0.06-0.89, p-trend: 0.04). CONCLUSIONS: Our study suggests that a high quality diet assessed by MD may decrease the risk of MS.

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)0.001
metaresearch head score (Gemma)0.005
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 score0.399
Threshold uncertainty score0.610

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
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 head0.103
GPT teacher head0.287
Teacher spread0.184 · 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

Citations78
Published2016
Admission routes1
Has abstractyes

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