MétaCan
Menu
Back to cohort
Record W2592378632 · doi:10.1016/j.jalz.2017.01.014

Consensus classification of posterior cortical atrophy

2017· article· en· W2592378632 on OpenAlexaff
Sebastian J. Crutch, Jonathan M. Schott, Gil D. Rabinovici, Melissa E. Murray, Julie S. Snowden, Wiesje M. van der Flier, Bradford C. Dickerson, Rik Vandenberghe, Samrah Ahmed, Bradley F. Boeve, Christopher Butler, Stefano F. Cappa, Mathieu Ceccaldi, Leonardo Cruz de Souza, Bruno Dubois, Olivier Félician, Douglas Galasko, Jonathan Graff‐Radford, Neill R. Graff‐Radford, Patrick R. Hof, Pierre Krolak‐Salmon, Manja Lehmann, Éloi Magnin, Mario F. Mendez, Peter J. Nestor, Chiadi U. Onyike, Victoria S. Pelak, Yolande A.L. Pijnenburg, Silvia Primativo, Martin N. Rossor, Natalie S. Ryan, Philip Scheltens, Timothy J. Shakespeare, Aida Suárez González, David F. Tang‐Wai, Keir Yong, María C. Carrillo, Nick C. Fox

Bibliographic record

VenueAlzheimer s & Dementia · 2017
Typearticle
Languageen
FieldNeuroscience
TopicGenetic Neurodegenerative Diseases
Canadian institutionsUniversity of TorontoUniversity Health Network
FundersNational Institutes of HealthNIHR Oxford Biomedical Research CentreAlzheimer NederlandBrain Research TrustAmerican College of RadiologyEngineering and Physical Sciences Research CouncilNational Institute of Neurological Disorders and StrokeNational Institute for Health and Care ResearchEconomic and Social Research CouncilAlzheimer’s Research UKMedical Research CouncilNational Institute on AgingAlzheimer's Association
KeywordsPosterior cortical atrophyAtrophyMedicineComputer sciencePathologyDiseaseDementia

Abstract

fetched live from OpenAlex

INTRODUCTION: A classification framework for posterior cortical atrophy (PCA) is proposed to improve the uniformity of definition of the syndrome in a variety of research settings. METHODS: Consensus statements about PCA were developed through a detailed literature review, the formation of an international multidisciplinary working party which convened on four occasions, and a Web-based quantitative survey regarding symptom frequency and the conceptualization of PCA. RESULTS: A three-level classification framework for PCA is described comprising both syndrome- and disease-level descriptions. Classification level 1 (PCA) defines the core clinical, cognitive, and neuroimaging features and exclusion criteria of the clinico-radiological syndrome. Classification level 2 (PCA-pure, PCA-plus) establishes whether, in addition to the core PCA syndrome, the core features of any other neurodegenerative syndromes are present. Classification level 3 (PCA attributable to AD [PCA-AD], Lewy body disease [PCA-LBD], corticobasal degeneration [PCA-CBD], prion disease [PCA-prion]) provides a more formal determination of the underlying cause of the PCA syndrome, based on available pathophysiological biomarker evidence. The issue of additional syndrome-level descriptors is discussed in relation to the challenges of defining stages of syndrome severity and characterizing phenotypic heterogeneity within the PCA spectrum. DISCUSSION: There was strong agreement regarding the definition of the core clinico-radiological syndrome, meaning that the current consensus statement should be regarded as a refinement, development, and extension of previous single-center PCA criteria rather than any wholesale alteration or redescription of the syndrome. The framework and terminology may facilitate the interpretation of research data across studies, be applicable across a broad range of research scenarios (e.g., behavioral interventions, pharmacological trials), and provide a foundation for future collaborative work.

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)0.061
metaresearch head score (Gemma)0.081
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.061
Threshold uncertainty score0.320

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0610.081
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.004
Bibliometrics0.0160.008
Science and technology studies0.0030.004
Scholarly communication0.0050.004
Open science0.0090.006
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0040.002

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 head0.083
GPT teacher head0.319
Teacher spread0.236 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations662
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueAlzheimer s & DementiaSame topicGenetic Neurodegenerative DiseasesFrench-language works237,207