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Record W4406437743 · doi:10.1093/braincomms/fcaf022

Biomarkers of disease progression in progressive supranuclear palsy for use in clinical trials

2024· review· en· W4406437743 on OpenAlexfundno aff
Cassandra Marotta, Benjamin Sinclair, Terence J. O’Brien, Lucy Vivash

Bibliographic record

VenueBrain Communications · 2024
Typereview
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsnot available
FundersMedical Research Future FundUCB PharmaNational Institutes of HealthDepartment of Industry, Science, Energy and Resources, Australian GovernmentNational Health and Medical Research CouncilEisai CanadaBiogenMedical Research CouncilEpilepsy SocietyEisaiU.S. Department of DefenseAustralian GovernmentMultiple Sclerosis Australia
KeywordsProgressive supranuclear palsyDiseaseMedicineClinical trialPhysical medicine and rehabilitationNeuroscienceInternal medicinePsychology

Abstract

fetched live from OpenAlex

Progressive supranuclear palsy (PSP) is a rare neurodegenerative disease with no current disease-modifying treatments approved. Longitudinal research and clinical trials for PSP are ongoing and require reliable measures that are sensitive to disease progression. Despite susceptibility to subjective limitations, clinical and cognitive assessments are the most used instruments in therapeutic trials in PSP. The objective of this review was to identify measures that have been studied longitudinally as measures of progression and are suitable for use as clinical trial endpoints. We reviewed the measures currently used as trial endpoints, identifying the clinical, cognitive, fluid and imaging measures that have previously been studied longitudinally, and discuss current diagnostic and emerging measures that are yet to be studied longitudinally but that may be sensitive to disease progression. We found that many fluid and imaging measures require further research to validate their use as longitudinal measures of change, including emerging measures that have not yet been studied specifically in PSP. We also summarize the sample size estimates required to detect changes in a two-arm, 52-week therapeutic trial and found that specific MRI volumes require the smallest sample sizes to detect change.

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)0.004
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.980
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.368
GPT teacher head0.551
Teacher spread0.184 · 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 designOther design
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

Citations8
Published2024
Admission routes1
Has abstractyes

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