MétaCan
Menu
Back to cohort
Record W4361191211 · doi:10.1093/brain/awad105

Progression of atypical parkinsonian syndromes: PROSPECT-M-UK study implications for clinical trials

2023· article· en· W4361191211 on OpenAlexfundno aff
Duncan Street, Edwin Jabbari, Alyssa Costantini, P Simon Jones, Negin Holland, Timothy Rittman, Marte Theilmann Jensen, Yen Yee Goh, Tong Guo, Amanda Heslegrave, Federico Roncaroli, Johannes Klein, Olaf Ansorge, Kieren Allinson, Zane Jaunmuktane, Tamas Revesz, Thomas T. Warner, Andrew J. Lees, Henrik Zetterberg, Lucy L. Russell, Martina Bocchetta, Jonathan D. Rohrer, David J. Burn, Nicola Pavese, Alexander Gerhard, Christopher Kobylecki, P. Nigel Leigh, Alistair Church, Henry Houlden, Huw R. Morris, James B. Rowe

Bibliographic record

VenueBrain · 2023
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsnot available
FundersReta Lila Weston Institute of Neurological Studies, UCL Queen Square Institute of Neurology,University College LondonEuropean Research CouncilDepartment of Health and Social CareMedical Research Council CanadaMedical Research CouncilHorizon 2020UK Dementia Research InstituteVetenskapsrådetFamiljen Erling-Perssons StiftelseHjärnfondenResearch Councils UKBiomedical Research FoundationParkinson's UKNational Institute for Health and Care ResearchWellcome TrustAlzheimer's SocietyAlzheimer's Drug Discovery FoundationBrain Research UKUniversity College London Hospitals NHS Foundation TrustMultiple System Atrophy TrustAlzheimer's AssociationStiftelsen för Gamla TjänarinnorBiomedical Research CouncilAlzheimer’s Research UKAlzheimer’s SocietyGuarantors of BrainWellcome
KeywordsProgressive supranuclear palsyParkinsonismCorticobasal degenerationClinical trialNeuroimagingMedicineAtrophyDiseasePsychologyPhysical therapyInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

The advent of clinical trials of disease-modifying agents for neurodegenerative disease highlights the need for evidence-based end point selection. Here we report the longitudinal PROSPECT-M-UK study of progressive supranuclear palsy (PSP), corticobasal syndrome (CBS), multiple system atrophy (MSA) and related disorders, to compare candidate clinical trial end points. In this multicentre UK study, participants were assessed with serial questionnaires, motor examination, neuropsychiatric and MRI assessments at baseline, 6 and 12 months. Participants were classified by diagnosis at baseline and study end, into Richardson syndrome, PSP-subcortical (PSP-parkinsonism and progressive gait freezing subtypes), PSP-cortical (PSP-frontal, PSP-speech and language and PSP-CBS subtypes), MSA-parkinsonism, MSA-cerebellar, CBS with and without evidence of Alzheimer's disease pathology and indeterminate syndromes. We calculated annual rate of change, with linear mixed modelling and sample sizes for clinical trials of disease-modifying agents, according to group and assessment type. Two hundred forty-three people were recruited [117 PSP, 68 CBS, 42 MSA and 16 indeterminate; 138 (56.8%) male; age at recruitment 68.7 ± 8.61 years]. One hundred and fifty-nine completed the 6-month assessment (82 PSP, 27 CBS, 40 MSA and 10 indeterminate) and 153 completed the 12-month assessment (80 PSP, 29 CBS, 35 MSA and nine indeterminate). Questionnaire, motor examination, neuropsychiatric and neuroimaging measures declined in all groups, with differences in longitudinal change between groups. Neuroimaging metrics would enable lower sample sizes to achieve equivalent power for clinical trials than cognitive and functional measures, often achieving N < 100 required for 1-year two-arm trials (with 80% power to detect 50% slowing). However, optimal outcome measures were disease-specific. In conclusion, phenotypic variance within PSP, CBS and MSA is a major challenge to clinical trial design. Our findings provide an evidence base for selection of clinical trial end points, from potential functional, cognitive, clinical or neuroimaging measures of disease progression.

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)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation 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 score0.139
Threshold uncertainty score0.422

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
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 head0.271
GPT teacher head0.526
Teacher spread0.255 · 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

Citations38
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueBrainSame topicParkinson's Disease Mechanisms and TreatmentsFrench-language works237,207