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Record W3147525977 · doi:10.1177/1744806921999025

Cellular and synaptic mechanisms for Parkinson’s disease-related chronic pain

2021· article· en· W3147525977 on OpenAlexafffund
Jing-Shan Lu, Qi‐Yu Chen, Xiang Chen, Xu‐Hui Li, Zhaoxiang Zhou, Qin Liu, Yuwan Lin, Miaomiao Zhou, Pingyi Xu, Min Zhuo

Bibliographic record

VenueMolecular Pain · 2021
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsUniversity of Toronto
FundersBasic and Applied Basic Research Foundation of Guangdong ProvinceNational Key Research and Development Program of ChinaCanadian Institutes of Health ResearchNational Natural Science Foundation of China
KeywordsDopaminergicDopamineParkinson's diseaseNeuroscienceAnterior cingulate cortexMedicineChronic painVentral tegmental areaDiseasePsychologyInternal medicineCognition

Abstract

fetched live from OpenAlex

Parkinson's disease is the second most common neurodegenerative disorder after Alzheimer's disease. Chronic pain is experienced by the vast majority of patients living with Parkinson's disease. The degeneration of dopaminergic neuron acts as the essential mechanism of Parkinson's disease in the midbrain dopaminergic pathway. The impairment of dopaminergic neurons leads to dysfunctions of the nociceptive system. Key cortical areas, such as the anterior cingulate cortex (ACC) and insular cortex (IC) that receive the dopaminergic projections are involved in pain transmission. Dopamine changes synaptic transmission via several pathway, for example the D2-adenly cyclase (AC)-cyclic AMP (cAMP)-protein kinase A (PKA) pathway and D1-G protein-coupled receptor kinase 2 (GRK2)-fragile X mental retardation protein (FMRP) pathway. The management of Parkinson's disease-related pain implicates maintenance of stable level of dopaminergic drugs and analgesics, however a more selective drug targeting at key molecules in Parkinson's disease-related pain remains to be investigated.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.614
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.008
GPT teacher head0.225
Teacher spread0.217 · 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.

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

Citations19
Published2021
Admission routes2
Has abstractyes

Explore more

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