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Record W4295765459 · doi:10.3390/healthcare10091772

Association between Vitamin D and Cognitive Deficiency in Alcohol Dependence

2022· article· en· W4295765459 on OpenAlexaboutno aff
V. Banjac, Goran Mihajlović, Nera Zivlak-Radulović, Lana Nežić, M. Mišković, Vesna Banjac

Bibliographic record

VenueHealthcare · 2022
Typearticle
Languageen
FieldMedicine
TopicVitamin D Research Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAssociation (psychology)CognitionPsychologyvitamin D deficiencyVitamin D and neurologyAlcoholClinical psychologyMedicinePsychiatryChemistryBiochemistryPsychotherapist

Abstract

fetched live from OpenAlex

There are still not enough findings to elucidate how exactly alcohol use impairs cognitive abilities. Some studies have shown that there is a link between alcohol intake and vitamin D levels, but these findings are inconsistent so further research is needed. The aim of this study was to investigate the association between serum vitamin D levels and cognitive impairment in alcohol-dependent individuals. A case-control study was carried out including a total of N = 132 respondents with a medical history of alcoholism, and healthy volunteers. The Montreal Cognitive Assessment (MoCa) and Addenbrooke’s Cognitive Examination-Revised (ACE-R) screening tools were used for cognitive status assessment and serum vitamin D levels analysis (blood samples of respondents). Significant difference (p = 0.022), was found in vitamin D levels in the alcohol-dependent group with cognitive deficiency 13.7 ± 9.4 (ng/mL), alcohol-dependent group without cognitive deficiency 19.5 ± 11.2 (ng/mL) and healthy controls 19.9 ± 11.1 (ng/mL), respectively. Furthermore, vitamin D levels were significantly different across all groups based on MoCa (p = 0.016) and ACE-R (p = 0.004) scores. All three groups exhibited vitamin D deficiency. A significant correlation was found between vitamin D deficiency and cognitive impairment, but it yielded no significant difference in alcohol-dependent individuals.

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)0.001
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 score0.017
Threshold uncertainty score0.308

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0000.000
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 head0.055
GPT teacher head0.378
Teacher spread0.323 · 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

Citations8
Published2022
Admission routes1
Has abstractyes

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