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Record W4322630198 · doi:10.2147/ijgm.s399307

Application of Periventricular White Matter Hyperintensities Combined with Homocysteine into Predicting Mild Cognitive Impairment in Parkinson’s Disease

2023· article· en· W4322630198 on OpenAlexaboutno aff
Zuowen Zhang, Shishuang Li, Shu‐Mei Wang

Bibliographic record

VenueInternational Journal of General Medicine · 2023
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineInternal medicineHyperintensityReceiver operating characteristicArea under the curveHomocysteineNeuropsychologyMultivariate analysisMontreal Cognitive AssessmentCognitive impairmentCognitionCardiologyConfoundingAudiologyDiseasePsychiatryMagnetic resonance imagingRadiology

Abstract

fetched live from OpenAlex

Purpose: To verify the associations between white matter hyperintensities (WMHs), plasma homocysteine (Hcy) levels, and mild cognitive impairment (MCI) in Parkinson’s disease (PD) patients and evaluate the predictive value of combination of WMHs and plasma Hcy levels for MCI. Patients and methods: In this study, 387 patients with PD were divided into MCI group and non-MCI group. Their cognition was evaluated with a comprehensive neuropsychological evaluation including 10 tests. Five cognitive domains, including the memory, attention/working memory, visuospatial, executive and language domains, were evaluated using two tests for each domain. MCI was determined when at least two tests demonstrated abnormal results, either one impaired test in two different cognitive domains or two impaired tests in a single cognitive domain. Multivariate analysis was performed to determine risk factors for MCI in PD patients. The receiver operating characteristic (ROC) curve was employed to assess the predictive values, and the Z test was employed to compare the area under curve (AUC). Results: MCI was identified in 195 PD patients with an incidence of 50.4%. Multivariate analysis results showed that PWMHs (OR: 5.162, 95% CI: 2.318~9.527), Hcy levels (OR: 1.189, 95% CI: 1.071~1.405) and MDS-UPDRS part III score (OR: 1.173, 95% CI: 1.062~1.394) were independently correlated with MCI in PD patients after adjusting for confounders. ROC curves showed that the AUCs of PWMHs, Hcy levels and their combination were 0.701 (SE: 0.026, 95% CI: 0.647~0.752), 0.688 (SE: 0.027, 95% CI: 0.635~0.742) and 0.879 (SE: 0.018, 95% CI: 0.844~0.915), respectively. Z test showed that the AUC of combination prediction was significantly higher than those of individual predictions (0.879 vs 0.701, Z =5.629, P < 0.001; 0.879 vs 0.688, Z =5.886, P < 0.001). Conclusion: The combination of WMHs and plasma Hcy levels could be applied in the prediction of MCI in PD patients. Keywords: Parkinson’s disease, mild cognitive impairment, white matter hyperintensities, homocysteine, prediction

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 score0.008
Threshold uncertainty score0.469

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.011
GPT teacher head0.270
Teacher spread0.259 · 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
Published2023
Admission routes1
Has abstractyes

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