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Record W4281250128 · doi:10.1002/mds.29056

Serum Inflammatory Markers and Progression of Nonmotor Symptoms in Early Parkinson's Disease

2022· article· en· W4281250128 on OpenAlexaboutno aff
Ryul Kim, Han‐Joon Kim, Jung Hwan Shin, Chan Young Lee, Seung Ho Jeon, Beomseok Jeon

Bibliographic record

VenueMovement Disorders · 2022
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsParkinson's diseaseDiseaseDegenerative diseaseMedicineNeurosciencePathologyBiology

Abstract

fetched live from OpenAlex

BACKGROUND: The influence of peripheral inflammation on nonmotor symptoms (NMSs) in Parkinson's disease (PD) remains unclear. OBJECTIVE: The aim of this study was to explore whether serum inflammatory marker profiles are associated with the progression of NMSs in early PD. METHODS: We included 45 patients with early PD and 20 healthy control subjects. Six inflammatory markers, including interleukin (IL)-1β, IL-2, IL-6, IL-10, tumor necrosis factor-α, and high-sensitivity C-reactive protein, were measured. NMSs were assessed using the Non-Motor Symptoms Scale, Montreal Cognitive Assessment, and Composite Autonomic Symptom Score-31 at baseline and after 3 years. RESULTS: Principal component (PC) analysis showed that only PC3 scores, mainly loaded by IL-2 and IL-6, were significantly elevated in the PD group compared with the control group. Higher PC3 scores in the PD group were associated with faster progression of Non-Motor Symptoms Scale total and mood/apathy domain scores. There were no significant associations of PC scores with Montreal Cognitive Assessment and Composite Autonomic Symptom Score-31 score changes. CONCLUSIONS: Peripheral inflammation may be related to the evolution of NMSs, particularly mood symptoms, in the early stages of PD. © 2022 International Parkinson and Movement Disorder Society.

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 score0.022
Threshold uncertainty score0.672

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 head0.005
GPT teacher head0.231
Teacher spread0.226 · 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

Citations28
Published2022
Admission routes1
Has abstractyes

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