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Record W2373395544

The relevant factors in patients with Parkinson disease and restless leg syndrome

2010· article· en· W2373395544 on OpenAlexaboutno aff
Ren Ya

Bibliographic record

VenueZhongguo shenjing jingshen jibing zazhi · 2010
Typearticle
Languageen
FieldMedicine
TopicRestless Legs Syndrome Research
Canadian institutionsnot available
Fundersnot available
KeywordsRestless legs syndromeHamdIncidence (geometry)Parkinson's diseaseInternal medicineDepression (economics)MedicineDiseasePediatricsPhysical therapyPsychologyNeurologySignificant differencePsychiatry
DOInot available

Abstract

fetched live from OpenAlex

Objective To study the features of patients with Parkinson's disease (PD) and restless legs syndrome (RLS) and to further examine relevant factors for the co-occurrence of PD and RLS.Methods One hundred and two PD patients were recruited in the present study.The RLS screen was further performed in PD patients based on the diagnostic criteria for RLS.PD patients with RLS were designated as the case group and PD patients without RLS served as a control group.The clinical data including general situation,medical history,clinical manifestations,severity score and the treatment were analyzed.Results There were 28 PD patients with RLS in total 102 PD patients and the incidence rate was 27.5%.All PD patients with RLS developed PD symptoms before RLS except one who had RLS symptoms prior to PD onset.HAMD score was significant statistical different between two groups (P0.05).Other parameters including the general situation,the main symptoms,L-dopa treatment,Montreal and UPDRS score were not significant different between two groups (P0.05).There was a negative correlation between age at onset of PD and the severity of RLS.Conclusions RLS in PD patients have a high incidence up to 27.5% and PD patients with RLS tend to develop depression.In PD patients with RLS,the younger onset,the more severe RLS is.

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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.013
GPT teacher head0.254
Teacher spread0.242 · 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

Citations0
Published2010
Admission routes1
Has abstractyes

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