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Plasma Osteoprotegerin and Cognitive Impairment after IschemicStroke

2025· article· en· W4407044198 on OpenAlexaboutno aff
Xinyue Chang, Pinni Yang, Yi Liu, Yu He, Xiaoli Qin, Beiping Song, Quan Yu, Jiawen Fei, Mengyao Shi, Daoxia Guo, Yanbo Peng, Jing Chen, Aili Wang, Tan Xu, Jiang He, Yonghong Zhang, Zhengbao Zhu

Bibliographic record

VenueCurrent Neurovascular Research · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBone Metabolism and Diseases
Canadian institutionsnot available
Fundersnot available
KeywordsOsteoprotegerinCognitive impairmentIschemic strokeStroke (engine)CognitionMedicineInternal medicineCardiologyIschemiaPsychiatryReceptor

Abstract

fetched live from OpenAlex

BACKGROUND: Plasma osteoprotegerin (OPG) has been linked to poor prognosis following stroke, but its impact on post-stroke cognitive impairment (PSCI) is unknown. The purpose of our work was to analyze the relationship of OPG with PSCI. METHODS: Our study included 613 ischemic stroke subjects with plasma OPG levels. We used the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) to assess PSCI. PSCI was defined as MMSE score <25 or MoCA score <23. RESULTS: =0.021), compared to that in the lowest tertile. We observed a positive linear relationship of plasma OPG levels with 3- month PSCI (P for linearity=0.046). Incorporating plasma OPG into conventional risk factors enhanced PSCI risk reclassification (all P <0.05). Consistent results were discovered when PSCI was evaluated using the MoCA score. CONCLUSION: High plasma OPG levels were related to an elevated risk of 3-month PSCI, indicating that OPG might be an effective biomarker for predicting PSCI.

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 score0.476
Threshold uncertainty score0.613

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 science0.0000.001
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 head0.022
GPT teacher head0.341
Teacher spread0.319 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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