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Record W3156098145 · doi:10.1002/mdc3.13220

Prevalence and Risk Factors for Double Vision in Parkinson Disease

2021· article· en· W3156098145 on OpenAlexaff
Ali G. Hamedani, Maureen G. Maguire, Connie Marras, Allison W. Willis

Bibliographic record

VenueMovement Disorders Clinical Practice · 2021
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsToronto Western HospitalUniversity of Toronto
FundersParkinson Study GroupMichael J. Fox Foundation for Parkinson's Research
KeywordsDiplopiaParkinson's diseaseMedicineDiseasePhysical therapyPhysical medicine and rehabilitationPediatricsAudiologyInternal medicineSurgery

Abstract

fetched live from OpenAlex

BACKGROUND: Some patients with Parkinson Disease (PD) report double vision, but its prevalence and determinants are unknown. OBJECTIVES: To determine the prevalence and risk factors for diplopia in PD. METHODS: Using data from 26,790 PD patients and 9257 controls in the Fox Insight Study, we compared the prevalence of diplopia using the Non-Movement Symptom Questionnaire. Associations with age, race, gender, disease duration, and scores on MDS-UPDRS part II, and Penn Parkinson's Daily Activity Questionnaire were assessed with generalized estimating equations. RESULTS: < 0.001) at baseline, and 28.2% of all PD patients reported diplopia at least once during the study (period prevalence). PD patients with diplopia were more likely to be older, non-white, have greater disease duration, and report greater motor, non-motor, and daily activity limitations. CONCLUSIONS: Diplopia is common and associates with motor and non-motor severity in PD.

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)0.002
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 score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.040
GPT teacher head0.386
Teacher spread0.345 · 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
Published2021
Admission routes1
Has abstractyes

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