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Record W4400349870 · doi:10.3390/biology13070503

Olfactory Function as a Potential Predictor of Cognitive Impairment in Men and Women

2024· article· en· W4400349870 on OpenAlexaboutno aff
Carla Masala, Francesco Loy, Ilenia Pinna, Nicoletta Aurora Manis, Tommaso Ercoli, Paolo Solla

Bibliographic record

VenueBiology · 2024
Typearticle
Languageen
FieldNeuroscience
TopicOlfactory and Sensory Function Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCognitionOlfactionOdorOlfactory systemAudiologyMontreal Cognitive AssessmentBiologyPsychologyCognitive impairmentCorrelationDevelopmental psychologyNeuroscienceMedicine

Abstract

fetched live from OpenAlex

BACKGROUND: Different previous studies indicated olfactory function as a predictor of several types of cognitive impairment, in particular related to neurodegenerative disease. However, scanty data are available on the role of odor threshold (OT), odor discrimination (OD), and odor identification (OI) as a predictor of cognitive impairment. The aim of this study was to evaluate potential correlations between each factor of the olfactory function versus each specific cognitive domain of the Montreal Cognitive Assessment (MoCA) test on healthy subjects in relation to gender and age. METHODS: Sniffin' Sticks and MoCA tests were used to determine olfactory function and cognitive abilities, respectively. RESULTS: In men, significant correlations were found in OT versus language index score and OI versus language and executive index score, while in women, OD and OI were correlated to visuospatial index score. CONCLUSIONS: Our data suggested that olfactory function (OT, OD, and OI) may be considered a predictor for cognitive impairment in relation to gender and age.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.831
Threshold uncertainty score0.251

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 head0.059
GPT teacher head0.270
Teacher spread0.210 · 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 designBench or experimental
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
Published2024
Admission routes1
Has abstractyes

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