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Record W4399295358 · doi:10.1002/mds.29866

Magnetic Resonance Imaging Measures to Track Atrophy Progression in Progressive Supranuclear Palsy in Clinical Trials

2024· article· en· W4399295358 on OpenAlexfundno aff
Andrea Quattrone, Nicolai Franzmeier, Hans‐Jürgen Huppertz, Martin Klietz, Sebastian Niclas Roemer, Adam L. Boxer, Johannes Levin, Günter U. Höglinger

Bibliographic record

VenueMovement Disorders · 2024
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsnot available
FundersNIH Clinical CenterUniversity of Chicago MedicineNewcastle upon Tyne Hospitals NHS Foundation TrustUniversity of California, San FranciscoDeutsches Zentrum für Neurodegenerative ErkrankungenUniversität RostockMedical Research CouncilCentre Hospitalier Universitaire de BordeauxServierEli Lilly and CompanyLudwig-Maximilians-Universität MünchenRWTH Aachen UniversityHumboldt-Universität zu BerlinH. Lundbeck A/SEberhard Karls Universität TübingenUniversidad de NavarraEisaiUniversity of TorontoUniversity of PennsylvaniaVolkswagen FoundationTechnische Universität DresdenKorea UniversityDeutsche ForschungsgemeinschaftUniversity of CambridgeJohns Hopkins UniversityGHR FoundationOchsner HealthIndiana University HealthNational Institutes of HealthRegeneron PharmaceuticalsRush UniversityUniversity of South FloridaPennsylvania State UniversityUniversity of South CarolinaMassachusetts General HospitalUniversity of MinnesotaUniversity of OttawaUniversity of ChicagoMorsani College of MedicineKorea University Guro HospitalBristol-Myers SquibbTeva Pharmaceutical IndustriesPfizerBiogenRheinische Friedrich-Wilhelms-Universität BonnAlzheimer's AssociationAlzheimer's Disease Neuroimaging InitiativeSanofiBayer Vital
KeywordsProgressive supranuclear palsyMagnetic resonance imagingAtrophyCohortMedicineNuclear medicinePsychologyInternal medicineRadiology

Abstract

fetched live from OpenAlex

BACKGROUND: Several magnetic resonance imaging (MRI) measures have been suggested as progression biomarkers in progressive supranuclear palsy (PSP), and some PSP staging systems have been recently proposed. OBJECTIVE: Comparing structural MRI measures and staging systems in tracking atrophy progression in PSP and estimating the sample size to use them as endpoints in clinical trials. METHODS: Progressive supranuclear palsy-Richardson's syndrome (PSP-RS) patients with one-year-follow-up longitudinal brain MRI were selected from the placebo arms of international trials (NCT03068468, NCT01110720, NCT01049399) and the DescribePSP cohort. The discovery cohort included patients from the NCT03068468 trial; the validation cohort included patients from other sources. Multisite age-matched healthy controls (HC) were included for comparison. Several MRI measures were compared: automated atlas-based volumetry (44 regions), automated planimetric measures of brainstem regions, and four previously described staging systems, applied to volumetric data. RESULTS: Of 508 participants, 226 PSP patients including discovery (n = 121) and validation (n = 105) cohorts, and 251 HC were included. In PSP patients, the annualized percentage change of brainstem and midbrain volume, and a combined index including midbrain, frontal lobe, and third ventricle volume change, were the progression biomarkers with the highest effect size in both cohorts (discovery: >1.6; validation cohort: >1.3). These measures required the lowest sample sizes (n < 100) to detect 30% atrophy progression, compared with other volumetric/planimetric measures and staging systems. CONCLUSIONS: This evidence may inform the selection of imaging endpoints to assess the treatment efficacy in reducing brain atrophy rate in PSP clinical trials, with automated atlas-based volumetry requiring smaller sample size than staging systems and planimetry to observe significant treatment effects. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.799
Threshold uncertainty score0.845

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.040
GPT teacher head0.385
Teacher spread0.344 · 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.

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

Citations23
Published2024
Admission routes1
Has abstractyes

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