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Comparison of amyloid burden in individuals with Down syndrome versus autosomal dominant Alzheimer's disease: a cross-sectional study
Anna H. Boerwinkle, Brian A. Gordon, Julie K. Wisch, Shaney Flores, Rachel L. Henson, Omar H. Butt, Nicole S. McKay, Charles D. Chen, Tammie L.S. Benzinger, Anne M. Fagan, Benjamin L. Handen, Bradley Christian, Elizabeth Head, Mark Mapstone, Michael S. Rafii, Sid E. O’Bryant, Florence Lai, H. Diana Rosas, Joseph H. Lee, Wayne Silverman, Adam M. Brickman, Jasmeer P. Chhatwal, Carlos Cruchaga, Richard J. Perrin, Chengjie Xiong, Jason Hassenstab, Eric McDade, Randall J. Bateman, Beau M. Ances, Howard Aizenstein, Howard Andrews, Karen L. Bell, Rasmus M. Birn, Peter Bulova, Amrita K. Cheema, Kewei Chen, I. C. H. Clare, Lorraine N. Clark, Ann D. Cohen, John N. Constantino, Eric Doran, Eleanor Feingold, Tatiana Foroud, Sigan L. Hartley, Christy Hom, Lawrence S. Honig, Miloš D. Ikonomović, Sterling C. Johnson, Courtney Jordan, M. Ilyas Kamboh, David B. Keator, William E. Klunk, Julia Kofler, William Charles Kreisl, Sharon J Krinsky- McHale, Patrick J. Lao, Charles M. Laymon, Ira T. Lott, Victoria Lupson, Chester A. Mathis, Davneet Minhas, Neelesh Nadkarni, Deborah Pang, Melissa Petersen, Julie C. Price, Eric M. Reiman, Batool Rizvi, Marwan N. Sabbagh, Nicole Schupf, Dana Tudorascu, Rameshwari V. Tumuluru, Benjamin Tycko, Badri Varadarajan, Desirée A. White, Michael A. Yassa, Shahid Zaman, Fan Zhang, Sarah Adams, Ricardo Allegri, Aki Araki, Nicolas R. Barthélemy, Jacob Bechara, Sarah Berman, Courtney Bodge, Susan E. Brandon, William S. Brooks, Jared R. Brosch, Jill Buck, Virginia Buckles, Kathleen Carter, Lisa Cash, Patricio Chrem Méndez, Jasmin Chua, Helena C. Chui, Laura Courtney, Gregory S. Day, Chrismary DeLaCruz, Darcy Denner, Anna Diffenbacher, Aylin Dincer, Tamara Donahue, Jane Douglas, Duc M. Duong, Noelia Egido, Bianca Esposito, Marty Farlow, Becca Feldman, Colleen Fitzpatrick, Nick C. Fox, Erin Franklin, Nelly Joseph‐Mathurin, Hisako Fujii, Samantha L. Gardener, Bernardino Ghetti, Alison Goate, Sarah B. Goldberg, Jill Goldman, Alyssa Gonzalez, Susanne Gräber‐Sultan, Neill R. Graff‐Radford, Morgan Graham, Julia Gray, Emily Gremminger, Miguel L. Grilo, Alex Groves, Christian Haass, Lisa Häslerc, Cortaiga Hellm, Elizabeth Herries, Laura Hoechst-Swisher, Anna Hofmann, David M. Holtzman, Russ C. Hornbeck, Yakushev Igor, Ryoko Ihara, Takeshi Ikeuchi, Snežana Ikonomović, Kenji Ishii, Clifford R. Jack, Gina Jerome, Erik C. B. Johnson, Mathias Jucker, Celeste M. Karch, Stephan Käser, Kensaku Kasuga, Sarah Keefe, Robert A. Koeppe, Deb Koudelis, Elke Kuder-Buletta, Christoph Laske, Allan I. Levey, Johannes Levin, Yan Li, Oscar L. López, Jacob Marsh, Ralph N. Martins, Neal Scott Mason, Colin L. Masters, Kwasi G. Mawuenyega, Austin McCullough, Arlene Mejia, Estrella Morenas‐Rodríguez, John C. Morris, James M. Mountz, Catherine J. Mummery, Akemi Nagamatsu, Katie Neimeyer, Yoshiki Niimi, James M. Noble, Joanne Norton, Brigitte Nuscher, Ulricke Obermüller, Antoinette O’Connor, Riddhi Patira, Lingyan Ping, Oliver Preische, Alan E. Renton, John M. Ringman, Stephen Salloway, Peter R. Schofield, Michio Senda, Nicholas T. Seyfried, Kristine Shady, Hiroyuki Shimada, Wendy Sigurdson, Jennifer S. Smith, Lori Smith, Beth E. Snitz, Hamid R. Sohrabi, Sochenda Stephens, Kevin Taddei, Sarah Thompson, Jonathan Vöglein, Peter Wang, Qīng Wáng, Elise A. Weamer, Jinbin Xu, Xu Xiong
Bibliographic record
VenueThe Lancet Neurology · 2022
Typearticle
Languageen
FieldMedicine
TopicDown syndrome and intellectual disability research
Canadian institutionsnot available
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute of Child Health and Human DevelopmentNIHR Cambridge Biomedical Research CentreInstituto de Salud Carlos IIIFonds de Recherche du Québec - SantéNational Institutes of HealthUK Dementia Research InstituteFleniDeutsches Zentrum für Neurodegenerative ErkrankungenEisaiUniversity of OxfordDepartment of Health and Social CareKorea Health Industry Development InstituteNational Institute on AgingNational Institute for Health and Care ResearchCanadian Institutes of Health ResearchFoundation for Barnes-Jewish HospitalJapan Agency for Medical Research and DevelopmentGHR FoundationFondation Brain CanadaU.S. Department of DefenseEli Lilly and CompanyBrightFocus FoundationHope Center for Neurological DisordersAlzheimer's AssociationBiogenAutism SpeaksUniversity College London Hospitals NHS Foundation Trust
KeywordsPSEN1Down syndromeAlzheimer's diseaseMedicinePresenilinApolipoprotein EPittsburgh compound BAmyloid (mycology)Amyloid precursor proteinDiseaseBiomarkerDementiaInternal medicinePathologyOncologyGeneticsBiologyPsychiatry
Abstract
No abstract in any covered source. Its absence is recorded, not treated as a negative.
No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.008
Threshold uncertainty score0.015
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 head0.101
GPT teacher head0.396
Teacher spread0.295 · how far apart the two teachers sit on this one work
Validation status
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine 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 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
Citations56
Published2022
Admission routes1
Has abstractno