MétaCan
Menu
Back to cohort
Record W2490618669 · doi:10.17925/usn.2014.10.02.90

Magnetic Resonance Imaging and Dopamine Transporter Single Photon Emission Tomography in Parkinson’s Disease—When Should We Use Them?

2014· article· en· W2490618669 on OpenAlexaff
Meir Kestenbaum, Cheryl Waters

Bibliographic record

VenuetouchREVIEWS in Neurology · 2014
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsCentre for Movement Disorders
Fundersnot available
KeywordsMedicineMagnetic resonance imagingDopamine transporterParkinson's diseaseDiseaseMedical diagnosisDopamineRadiologyPathologyInternal medicineDopaminergic

Abstract

fetched live from OpenAlex

Making an accurate diagnosis of Parkinson’s disease (PD) is critical for patient counseling and therapeutic management. The diagnosis of PD remains primarily clinical and is based on nonspecific findings on examination. Magnetic resonance imaging and nuclear imaging of the brain can assist in making timely diagnoses and managing treatment appropriately. These tests should not be used routinely, but only in a selected subgroup of patients where the diagnosis of PD is not clear

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 score0.403
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 head0.033
GPT teacher head0.260
Teacher spread0.227 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

Explore more

Same venuetouchREVIEWS in NeurologySame topicParkinson's Disease Mechanisms and TreatmentsFrench-language works237,207