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Record W3099921742 · doi:10.1002/trc2.12057

CCCDTD5 recommendations on early and timely assessment of neurocognitive disorders using cognitive, behavioral, and functional scales

2020· article· en· W3099921742 on OpenAlexaffabout
David F. Tang‐Wai, Eric E. Smith, Marie‐Andrée Bruneau, Amer M. Burhan, Atri Chatterjee, Howard Chertkow, Samira Choudhury, Ehsan Dorri, Simon Ducharme, Corinne E. Fischer, Sheena Ghodasara, Nathan Herrmann, Ging‐Yuek Robin Hsiung, Sanjeev Kumar, Robert Laforce, Linda Lee, Fadi Massoud, Kenneth I. Shulman, Michael Stiffel, Serge Gauthier, Zahinoor Ismail

Bibliographic record

VenueAlzheimer s & Dementia Translational Research & Clinical Interventions · 2020
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsMcGill UniversityInstitut Universitaire de Gériatrie de MontréalMcMaster UniversityUniversité LavalSunnybrook Health Science CentreSt. Michael's HospitalMcGill University Health CentreUniversity Health NetworkUniversity of AlbertaCentre for Addiction and Mental HealthBaycrest HospitalMontreal Heart InstituteUniversity of British ColumbiaMontreal Neurological Institute and HospitalParkwood InstituteUniversité de SherbrookeUniversity of TorontoWestern UniversityOntario Brain InstituteUniversité de MontréalHotchkiss Brain InstituteHealth Sciences CentreUniversity of CalgaryToronto Rehabilitation Institute
Fundersnot available
KeywordsDementiaNeurocognitiveCognitionCognitive declineContext (archaeology)MedicineClinical psychologyPsychological interventionPsychiatryGrading (engineering)DiseasePsychology

Abstract

fetched live from OpenAlex

INTRODUCTION: Earlier diagnosis of neurocognitive disorders and neurodegenerative disease is needed to implement preventative interventions, minimize harm, and reduce risk of exploitation in the context of undetected disease. Along the spectrum from subjective cognitive decline (SCD) to dementia, evidence continues to emerge with respect to detection, staging, and monitoring. Updates to previous guidelines are required for clinical practice. METHODS: A subcommittee of the 5th Canadian Consensus Conference on Diagnosis and Treatment of Dementia (CCCDTD) reviewed emerging evidence to address the following: (1) Is there a role for screening at-risk patients without clinical concerns? In what context is assessment for dementia appropriate? (2) What tools can be used to evaluate patients in whom cognitive decline is suspected? (3) What important information can be gained from an informant, using which measures? (4) What instruments can be used to get more in-depth information to diagnose mild cognitive impairment (MCI) or dementia? (5) What is the approach to those with cognitive concerns but without objective changes (ie, SCD)? (6) How do we track response to treatment and change over time? The Grading of Recommendations Assessment, Development, and Evaluation system was used to rate quality of the evidence and strength of the recommendations. RESULTS: We recommend instruments to assess and monitor cognition, behavior, and function across the cognitive spectrum, including reports from patient and informant. We recommend against screening asymptomatic older adults but recommend investigation for self- or informant reports of changes in cognition, emergence of behavioral or psychiatric symptoms, or decline in function or self-care. Standardized assessments should be used for cognitive and behavioral change that have sufficient validity for use in clinical practice. DISCUSSION: The CCCDTD5 provides evidence-based recommendations for detection, assessment, and monitoring of neurocognitive disorders. Although these guidelines were developed for use in Canada, they may also be useful in other jurisdictions.

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)0.044
metaresearch head score (Gemma)0.194
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: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.158
Threshold uncertainty score0.314

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0440.194
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0040.012
Bibliometrics0.0110.006
Science and technology studies0.0040.003
Scholarly communication0.0060.005
Open science0.0130.007
Research integrity0.0220.014
Insufficient payload (model declined to judge)0.0200.011

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 head0.473
GPT teacher head0.564
Teacher spread0.091 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations31
Published2020
Admission routes2
Has abstractyes

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