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Record W3005329824 · doi:10.1177/0891988720901787

Association Between Somatization and Nonmotor Symptoms Severity in People With Parkinson Disease

2020· article· en· W3005329824 on OpenAlexaboutno aff
Aranza Polo-Morales, Ángel Alcocer-Salas, Mayela Rodríguez‐Violante, Daniella Pinto-Solís, Rodolfo Solís‐Vivanco, Amin Cervantes‐Arriaga

Bibliographic record

VenueJournal of Geriatric Psychiatry and Neurology · 2020
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsSomatizationParkinson's diseaseQuality of life (healthcare)Montreal Cognitive AssessmentRating scaleMedicinePsychologyDiseasePsychiatryPhysical therapyCognitive impairmentCognitionInternal medicineAnxiety

Abstract

fetched live from OpenAlex

Objective: To assess the frequency of somatization and its association with motor, nonmotor symptoms, and quality of life in persons with Parkinson disease (PD). Methods: A cross-sectional case–control study was carried out. Assessments included the List of 90 Symptoms somatic factor (SCL-90-R SOM), Movement Disorder Society Unified Parkinson’s Ratings Scale (MDS-UPDRS), Non-Motor Symptom Scale (NMSS), Montreal Cognitive Assessment (MoCA), and Parkinson Questionnaire-8 (PDQ-8). Results: A total 93 persons with PD and 93 controls were included. Somatization within the PD group was 2 times more frequent compared to the control group (43% vs 21.5%, P = .003). Persons with PD had higher NMSS total scores (48.6 ± 42.6 vs 28.3 ± 30.4, P = .001). Patients with PD with somatization had worst MDS-UPDRS, NMSS, MoCA, and PDQ-8 (all P < .05). Conclusion: Somatization is more frequent in persons with PD compared to healthy controls. Somatization in PD is associated with nonmotor symptoms and worst quality of life.

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)0.001
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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.006
GPT teacher head0.222
Teacher spread0.215 · 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".

Quick stats

Citations12
Published2020
Admission routes1
Has abstractyes

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Same venueJournal of Geriatric Psychiatry and NeurologySame topicParkinson's Disease Mechanisms and TreatmentsFrench-language works237,207