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Record W4401383018 · doi:10.1212/wnl.0000000000209699

Long-Term Dementia Risk in Parkinson Disease

2024· article· en· W4401383018 on OpenAlexaboutno aff
Julia Gallagher, Caroline Gochanour, Chelsea Caspell‐Garcia, Roseanne D. Dobkin, Dag Aarsland, Roy N. Alcalay, Matthew J. Barrett, Lana M. Chahine, Alice Chen‐Plotkin, Christopher S. Coffey, Nabila Dahodwala, Jamie L. Eberling, Alberto J. Espay, James B. Leverenz, Irene Litvan, Eugenia Mamikonyan, James F. Morley, Irene Hegeman Richard, Liana S. Rosenthal, Andrew Siderowf, Tatyana Simuni, Michele K. York, Allison W. Willis, Sharon X. Xie, Daniel Weintraub, Kenneth Marek, Caroline Tanner, Tanya Simuni, Douglas Galasko, Kalpana Merchant, Kathleen L. Poston, Tatiana Foroud, Brit Mollenhauer, Dan Weintraub, Ethan Brown, Karl Kieburtz, Duygu Tosun, Werner Poewe, Susan Bressman, Raymond James, Ekemini Riley, John Seibyl, Leslie M. Shaw, David G. Standaert, Sneha Mantri, Michael A. Schwarzschild, Connie Marras, Hubert Fernandez, Ira Shoulson, Helen Rowbotham, Lucy Norcliffe‐Kaufmann, Paola Casalin, Claudia Trenkwalder, Todd Sherer, Sohini Chowdhury, Mark Frasier, Katie Kopil, Alyssa O’Grady, James Gibaldi, Maggie Kuhl, L. Kirsch, Emily Flagg, Bridget McMahon, Craig Stanley, Kim Fabrizio, Dixie Ecklund, Trevis Huff, Richard M. Peters, Janel Fedler, Laura Heathers, Christopher Hobbick, Gena Antonopoulos, Michael C. Brumm, Arthur W. Toga, Karen Crawford, Andrew Singleton, Thomas J. Montine, Monica Korell, Ruth B. Schneider, Kelvin L. Chou, David Russell, Stewart A. Factor, Penelope Hogarth, Robert A. Hauser, Marie Saint‐Hilaire, David Shprecher, Kathrin Brockmann, Yen Tai, Paolo Barone, Stuart Isaacson, María José Martí, Eduardo Tolosa, Shu‐Ching Hu, Emile Moukheiber, Jean-Christophe Corvol, Nir Giladi, Javier Ruiz‐Martínez, Jan Aasly, Leonidas Stefanis, Karen Marder, Arjun Tarakad, Tiago Mestre, Aleksandar Videnović, Rajesh Pahwa, Mark Lew, Holly A. Shill, Amy W. Amara, Charles H. Adler, Maureen A. Leehey, Giulietta Riboldi, Nikolaus R. McFarland, Ron Postuma, Zoltán Mari, Nicola Pavese, Norbert Brüggemann, Christine Klein, Bastiaan R. Bloem, Anisha G. Singh, Angela Stovall, Lianne Ramia, Katrina Wakeman, Karen Williams, Courtney Blair, Krista Specketer, Diana Willeke, Jennifer Mule, Ella Hilt, Shawnees Peacock, Kori Ribb, Susan Ainscough, Lisbeth Pennente, Julia Brown, Christina Gruenwald, Barbara Sommerfeld, Farah Kausar, Alícia Garrido, Deborah Raymond, Ioana Croitoru, Anne Grete Kristiansen, Helen Mejia‐Santana, Anjana Singh, Danica Nogo, Shawna Reddie, Samantha Murphy, Lauren O’Brien, Fnu Madhuri, Daniel Freire, Farah Ismail, Tom Osgood, Heidi Friedeck, Jenny Frisendahl, Ying Liu, Caitlin Romano, Kelly Clark, Kyle Rizer, Stéphanie Carvalho, Sherri Mosovsky, Farah Nadiah Sulaiman, Dora Valent, Raquel Lopes, Michelle Torreliza, Victoria K. Foster, Madita Grümmer, Myrthe M. Burgler, Christos Koros, Jamil Razzaque

Bibliographic record

VenueNeurology · 2024
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsnot available
FundersNational Institute on AgingNational Institutes of HealthEisaiCHDI FoundationACADIA PharmaceuticalsParkinson's FoundationUniversity of PennsylvaniaInternational Parkinson and Movement Disorder SocietyBiogenNational Institute of Neurological Disorders and StrokeMassachusetts General HospitalSanofiU.S. Department of Veterans Affairs
KeywordsDementiaObservational studyMedicineParkinson's diseaseDiseaseTerm (time)Alzheimer's diseaseGerontologyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND AND OBJECTIVES: It is widely cited that dementia occurs in up to 80% of patients with Parkinson disease (PD), but studies reporting such high rates were published over two decades ago, had relatively small samples, and had other limitations. We aimed to determine long-term dementia risk in PD using data from two large, ongoing, prospective, observational studies. METHODS: Participants from the Parkinson's Progression Markers Initiative (PPMI), a multisite international study, and a long-standing PD research cohort at the University of Pennsylvania (Penn), a single site study at a tertiary movement disorders center, were recruited. PPMI enrolled de novo, untreated PD participants and Penn a convenience cohort from a large clinical center. For PPMI, a cognitive battery is administered annually, and a site investigator makes a cognitive diagnosis. At Penn, a comprehensive cognitive battery is administered either annually or biennially, and a cognitive diagnosis is made by expert consensus. Interval-censored survival curves were fit for time from PD diagnosis to stable dementia diagnosis for each cohort, using cognitive diagnosis of dementia as the primary end point and Montreal Cognitive Assessment (MoCA) score <21 and Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) Part I cognition score ≥3 as secondary end points for PPMI. In addition, estimated dementia probability by PD disease duration was tabulated for each study and end point. RESULTS: For the PPMI cohort, 417 participants with PD (mean age 61.6 years, 65% male) were followed, with an estimated probability of dementia at year 10 disease duration of 9% (site investigator diagnosis), 15% (MoCA), or 12% (MDS-UPDRS Part I cognition). For the Penn cohort, 389 participants with PD (mean age 69.3 years, 67% male) were followed, with 184 participants (47% of cohort) eventually diagnosed with dementia. The interval-censored curve for the Penn cohort had a median time to dementia of 15 years (95% CI 13-15); the estimated probability of dementia was 27% at 10 years of disease duration, 50% at 15 years, and 74% at 20 years. DISCUSSION: Results from two large, prospective studies suggest that dementia in PD occurs less frequently, or later in the disease course, than previous research studies have reported.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.487

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.014
GPT teacher head0.272
Teacher spread0.259 · 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

Citations74
Published2024
Admission routes1
Has abstractyes

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