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Record W7043431661

Subclinical Hypothyroidism is Associated with Cognitive Impairment in Patients with Cerebral Small Vessel Disease

2023· article· en· W7043431661 on OpenAlexaboutno aff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2023
Typearticle
Languageen
FieldMaterials Science
TopicX-ray Diffraction in Crystallography
Canadian institutionsnot available
Fundersnot available
KeywordsConfoundingSubclinical infectionCognitionLogistic regressionMediationDiseaseEffects of sleep deprivation on cognitive performanceCognitive impairment
DOInot available

Abstract

fetched live from OpenAlex

Zhenjie Teng,1– 3,* Jing Feng,4,* Peiyuan Lv1– 3 1Department of Neurology, Hebei Medical University, Shijiazhuang, People’s Republic of China; 2Department of Neurology, Hebei General Hospital, Shijiazhuang, People’s Republic of China; 3Hebei Provincial Key Laboratory of Cerebral Networks and Cognitive Disorders, Shijiazhuang, People’s Republic of China; 4Department of Endocrinology, Hebei General Hospital, Shijiazhuang, People’s Republic of China*These authors contributed equally to this workCorrespondence: Peiyuan Lv, Department of Neurology, Hebei General Hospital, 348 Heping West Road, Shijiazhuang, Hebei Province, 050051, People’s Republic of China, Tel/Fax +86 31185988906, Email peiyuanlu2@163.comObjective: To investigate the association between subclinical hypothyroidism (SCH) and cognitive function in patients with cerebral small vessel disease (CSVD).Methods: We evaluated 528 patients with CSVD in this retrospective study. SCH was defined as elevated levels of thyroid stimulating hormone with normal concentrations of free thyroxine. Magnetic resonance imaging was performed to assess the total CSVD burden score and the Montreal Cognitive Assessment was used to measure the cognitive function. Participants were grouped based on cognitive function or total CSVD burden score. Multivariate logistic regression and mediation analysis models were used to estimate the association of SCH with cognitive function and CSVD burden.Results: SCH was an independent risk factor for cognitive impairment in patients with CSVD after adjustment for potential confounding factors (OR: 1.939; 95% CI: 1.170 to 3.213; P=0.010). Additionally, SCH was independently associated with severe CSVD burden after adjustment for potential confounding factors (OR: 1.668; 95% CI: 1.085 to 2.564; P=0.020). Mediation analysis found a significant moderating effect (P=0.021) of the severe CSVD burden on the relation between SCH and cognitive impairment after adjustment for potential confounding factors. A 30.1% of the total effect between this relation was attributable to the presence of severe CSVD burden.Conclusion: SCH was associated with an increased risk of cognitive impairment in patients with CSVD. The mediating role of severe CSVD burden suggests that SCH may lead to cognitive impairment through the presence of severe CSVD burden. These findings may suggest strategies for screening for SCH in the context of cognitive impairment in patients with severe CSVD.Keywords: subclinical hypothyroidism, cognitive function, cerebral small vessel disease

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.152
GPT teacher head0.472
Teacher spread0.320 · 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 source (direct Gemma or distilled Codex), 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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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