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Record W2616800762 · doi:10.1002/mds.27034

Which ante mortem clinical features predict progressive supranuclear palsy pathology?

2017· review· en· W2616800762 on OpenAlexaff
Gesine Respondek, Carolin Kurz, Thomas Arzberger, Yaroslau Compta, Elisabet Englund, Leslie W. Ferguson, Ellen Gelpí, Armin Giese, David J. Irwin, Wassilios G. Meissner, Christer Nilsson, Alexander Pantelyat, Alex Rajput, John C. van Swieten, Claire Troakes, Keith A. Josephs, Anthony E. Lang, Brit Mollenhauer, Ulrich Müller, Jennifer L. Whitwell, Angelo Antonini, Kailash P. Bhatia, Yvette Bordelon, Jean‐Christophe Corvol, Carlo Colosimo, Richard Dodel, Murray Grossman, Jan Kassubek, Florian Krismer, Johannes Levin, Stefan Lorenzl, Huw R. Morris, Peter J. Nestor, Wolfgang H. Oertel, Gil D. Rabinovici, James B. Rowe, Thilo van Eimeren, Gregor K. Wenning, Adam L. Boxer, Lawrence I. Golbe, Irene Litvan, María Stamelou, Günter U. Höglinger

Bibliographic record

VenueMovement Disorders · 2017
Typereview
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsUniversity of SaskatchewanToronto Western HospitalRoyal University Hospital
FundersDeutsche ForschungsgemeinschaftNational Institute on AgingNational Institute of Neurological Disorders and StrokeMedical Research CouncilNational Institute on Deafness and Other Communication DisordersWellcome TrustCurePSP
KeywordsProgressive supranuclear palsyMedicinePathologyNeurosciencePsychologyDisease

Abstract

fetched live from OpenAlex

BACKGROUND: Progressive supranuclear palsy (PSP) is a neuropathologically defined disease presenting with a broad spectrum of clinical phenotypes. OBJECTIVE: To identify clinical features and investigations that predict or exclude PSP pathology during life, aiming at an optimization of the clinical diagnostic criteria for PSP. METHODS: We performed a systematic review of the literature published since 1996 to identify clinical features and investigations that may predict or exclude PSP pathology. We then extracted standardized data from clinical charts of patients with pathologically diagnosed PSP and relevant disease controls and calculated the sensitivity, specificity, and positive predictive value of key clinical features for PSP in this cohort. RESULTS: Of 4166 articles identified by the database inquiry, 269 met predefined standards. The literature review identified clinical features predictive of PSP, including features of the following 4 functional domains: ocular motor dysfunction, postural instability, akinesia, and cognitive dysfunction. No biomarker or genetic feature was found reliably validated to predict definite PSP. High-quality original natural history data were available from 206 patients with pathologically diagnosed PSP and from 231 pathologically diagnosed disease controls (54 corticobasal degeneration, 51 multiple system atrophy with predominant parkinsonism, 53 Parkinson's disease, 73 behavioral variant frontotemporal dementia). We identified clinical features that predicted PSP pathology, including phenotypes other than Richardson's syndrome, with varying sensitivity and specificity. CONCLUSIONS: Our results highlight the clinical variability of PSP and the high prevalence of phenotypes other than Richardson's syndrome. The features of variant phenotypes with high specificity and sensitivity should serve to optimize clinical diagnosis of PSP. © 2017 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 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)0.005
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0080.006
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.069
GPT teacher head0.407
Teacher spread0.338 · 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 designSystematic review
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

Citations164
Published2017
Admission routes1
Has abstractyes

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