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Record W2112646944 · doi:10.4061/2011/292719

Development of a Non-Motor Fluctuation Assessment Instrument for Parkinson Disease

2011· article· en· W2112646944 on OpenAlexaff
Galit Kleiner‐Fisman, Rebecca Martine, Anthony E. Lang, Matthew B. Stern

Bibliographic record

VenueParkinson s Disease · 2011
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsToronto Western HospitalBaycrest HospitalUniversity of Toronto
Fundersnot available
KeywordsMotor symptomsParkinson's diseaseQuality of life (healthcare)DiseaseLevodopaMotor dysfunctionPhysical medicine and rehabilitationExpert opinionMotor skillMedicineMotor activityPhysical therapyPsychologyNeuroscienceIntensive care medicineInternal medicine

Abstract

fetched live from OpenAlex

Patients with Parkinson disease are increasingly recognized to suffer from non-motor symptoms in addition to motor symptoms. Many non-motor symptoms fluctuate in parallel with motor symptoms and in relationship to plasma levodopa levels. Though these symptoms are troublesome and result in reduced quality of life to patients and their caregivers, there has not been an objective method of recognizing and quantifying non-motor fluctuations (NMFs). This study sought to develop a patient-based instrument that would accurately capture the experience of patients with NMFs. Patient-based nominal group technique sessions, focus groups, and expert opinion were utilized in developing this questionnaire.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.269
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.043
GPT teacher head0.300
Teacher spread0.258 · 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 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

Citations35
Published2011
Admission routes1
Has abstractyes

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