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Diagnosis and management of dementia with Lewy bodies
Ian G. McKeith, Bradley F. Boeve, Dennis W. Dickson, Glenda M. Halliday, John‐Paul Taylor, Daniel Weintraub, Dag Aarsland, James E. Galvin, Johannes Attems, Clive Ballard, Ashley Bayston, Thomas G. Beach, Frederic Blanc, Nicolaas I. Bohnen, Laura Bonanni, José Brás, Patrik Brundin, David J. Burn, Alice Chen‐Plotkin, John E. Duda, Omar M. A. El‐Agnaf, Howard Feldman, Tanis J. Ferman, Dominic ffytche, Hiroshige Fujishiro, Douglas Galasko, Jennifer G. Goldman, Stephen N. Gomperts, Neill R. Graff‐Radford, Lawrence S. Honig, Álex Iranzo, Kejal Kantarci, Daniel Kaufer, Walter A. Kukull, Virginia M.‐Y. Lee, James B. Leverenz, Simon J.G. Lewis, Carol F. Lippa, Angela Lunde, Mario Masellis, Eliezer Masliah, Pamela J. McLean, Brit Mollenhauer, Thomas J. Montine, Emilio Moreno, Etsuro Mori, Melissa E. Murray, John T. O’Brien, Sotoshi Orimo, Ronald B. Postuma, Shankar Ramaswamy, Owen A. Ross, David P. Salmon, Andrew Singleton, Angela Taylor, Alan Thomas, Pietro Tiraboschi, Jon B. Toledo, John Q. Trojanowski, Debby W. Tsuang, Zuzana Walker, Masahito Yamada, Kenji Kosaka
Bibliographic record
VenueNeurology · 2017
Typereview
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsnot available
FundersNational Institute of Neurological Disorders and StrokeNational Institute for Dementia ResearchNational Institute on AgingNIHR Newcastle Biomedical Research CentreProgramme Grants for Applied ResearchNational Health and Medical Research CouncilMedical Research CouncilCanadian Institutes of Health ResearchQatar Biomedical Research Institute, Hamad Bin Khalifa UniversityHouston Methodist Research InstituteAvid RadiopharmaceuticalsPerelman School of Medicine, University of PennsylvaniaGenentechUniversité de StrasbourgH. Lundbeck A/SU.S. Department of Health and Human ServicesTauRx PharmaceuticalsNewcastle upon Tyne Hospitals NHS Foundation TrustEisaiMinistero della SaluteUniversity College LondonCentre National de la Recherche ScientifiqueKing's College LondonUniversity of ExeterNational Institute for Health and Care ResearchUniversity of OxfordACADIA PharmaceuticalsFondation Brain CanadaPfizerUniversity of WashingtonTeva Pharmaceutical IndustriesBristol-Myers SquibbCleveland ClinicCurePSPMassachusetts General HospitalLittle Family FoundationU.S. Department of Veterans AffairsUniversity of PennsylvaniaAlzheimer's Drug Discovery FoundationNational Institutes of HealthKanazawa UniversityDemensförbundetFlorida Department of HealthThomas Jefferson UniversityCHDI FoundationInternational Parkinson and Movement Disorder SocietyBiogenRush UniversityUniversity of TorontoUniversity of North Carolina at Chapel HillVan Andel Research InstituteUniversidad del AtlánticoUniversity of California, San DiegoHouston Methodist HospitalMinistero dello Sviluppo EconomicoNewcastle UniversityHeart and Stroke Foundation of CanadaSanofiWeston Brain InstituteCalifornia Institute for Regenerative MedicineFlorida Atlantic UniversityEli Lilly and Company
KeywordsDementia with Lewy bodiesNeurocognitiveREM sleep behavior disorderMedicineClinical trialNeuroimagingDementiaDiseaseLewy bodyPsychiatryIntensive care medicinePsychologyParkinson's diseaseNeurosciencePathologyCognition
Abstract
OpenAlex records an abstract for this work, but it could not be fetched just now.
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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.011
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 head0.077
GPT teacher head0.347
Teacher spread0.270 · 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 designNot applicable
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
Citations4,446
Published2017
Admission routes1
Has abstractyes