MétaCan
Menu
Back to cohort
Record W2292106802 · doi:10.1586/14737175.2015.1083423

The relevance of pre-motor symptoms in Parkinson’s disease

2015· review· en· W2292106802 on OpenAlexaff
Naomi P. Visanji, Connie Marras

Bibliographic record

VenueExpert Review of Neurotherapeutics · 2015
Typereview
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsToronto Western Hospital
Fundersnot available
KeywordsParkinson's diseaseDiseaseMotor symptomsMedicinePathologicalMoodClinical significanceNeurosciencePsychologyPhysical medicine and rehabilitationPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

Parkinson's disease (PD) has a wide range of non-motor symptoms including; constipation, sleep disturbance, deficits in vision and olfaction, mood disorders and cardiac autonomic dysfunction. Several of these non-motor symptoms can manifest prior to the onset of motor symptoms. Recognizing these pre-motor symptoms may enable early diagnosis of PD. Currently, no single pre-motor symptom is able to predict the development of PD with 100% sensitivity or specificity. Ongoing studies in several independent at-risk cohorts should reveal the potential of combinations of pre-motor symptoms and multi-stage screening strategies to identify individuals at increased risk of PD. PD progression may be governed by a prion-like spread of a-syn throughout the nervous system. Identifying individuals at the earliest stage will likely be critical to preventing the pathological progression of PD, highlighting the relevance of pre-motor symptoms in the future treatment of the disease.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.002

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 head0.053
GPT teacher head0.383
Teacher spread0.330 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations36
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueExpert Review of NeurotherapeuticsSame topicParkinson's Disease Mechanisms and TreatmentsFrench-language works237,207