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Record W2547869435 · doi:10.1016/j.jalz.2016.09.012

Implementation of subjective cognitive decline criteria in research studies

2016· article· en· W2547869435 on OpenAlexafffund
José Luís Molinuevo, Laura A. Rabin, Rebecca E. Amariglio, Rachel F. Buckley, Bruno Dubois, Kathryn A. Ellis, Michael Ewers, Harald Hampel, Stefan Klöppel, Lorena Rami, ‌Barry Reisberg, Andrew J. Saykin, Sietske A.M. Sikkes, Colette M. Smart, Beth E. Snitz, Reisa A. Sperling, Wiesje M. van der Flier, Michael Wagner, Frank Jessen

Bibliographic record

VenueAlzheimer s & Dementia · 2016
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsUniversity of Victoria
FundersNational Institute on AgingInstituto de Salud Carlos IIISchool of Medicine, New York UniversityFondation pour la Recherche sur AlzheimerEU Joint Programme – Neurodegenerative Disease ResearchAlzheimer NederlandGIESKES-STRIJBIS FONDSUniversité Pierre et Marie CurieAXA Research FundAgence Nationale de la RechercheEuropean Regional Development FundEuropean CommissionYork UniversityNational Institutes of HealthFisher Center for Alzheimer's Research FoundationU.S. Department of Health and Human Services
KeywordsGeneralizability theoryOperationalizationComparabilityCognitive declineContext (archaeology)CognitionInclusion and exclusion criteriaPsychologyDiseaseInclusion (mineral)MedicineClinical psychologyGerontologyDevelopmental psychologyDementiaPsychiatryAlternative medicinePathologySocial psychology

Abstract

fetched live from OpenAlex

INTRODUCTION: Subjective cognitive decline (SCD) manifesting before clinical impairment could serve as a target population for early intervention trials in Alzheimer's disease (AD). A working group, the Subjective Cognitive Decline Initiative (SCD-I), published SCD research criteria in the context of preclinical AD. To successfully apply them, a number of issues regarding assessment and implementation of SCD needed to be addressed. METHODS: Members of the SCD-I met to identify and agree on topics relevant to SCD criteria operationalization in research settings. Initial ideas and recommendations were discussed with other SCD-I working group members and modified accordingly. RESULTS: Topics included SCD inclusion and exclusion criteria, together with the informant's role in defining SCD presence and the impact of demographic factors. DISCUSSION: Recommendations for the operationalization of SCD in differing research settings, with the aim of harmonization of SCD measurement across studies are proposed, to enhance comparability and generalizability across studies.

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)0.627
metaresearch head score (Gemma)0.628
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: Methods
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.373
Threshold uncertainty score0.459

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.6270.628
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.004
Bibliometrics0.0110.010
Science and technology studies0.0040.009
Scholarly communication0.0110.010
Open science0.0070.011
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0020.001

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 head0.163
GPT teacher head0.504
Teacher spread0.341 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designTheoretical or conceptual
DomainMethods
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

Citations587
Published2016
Admission routes2
Has abstractyes

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