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Impact of a National Dementia Research Program – the CCNA (Canadian Consortium on Neurodegeneration in Aging) (2342)

2020· article· en· W3159794664 on OpenAlexaffabout
Howard Chertkow, Nathalie Bélanger, Jennifer Bethell, Sandra E. Black, Michael Borrie, Roger A. Dixon, Gillian Einstein, Howard Feldman, Serge Gauthier, David B. Hogan, Mario Masellis, Colleen J. Maxwell, Katherine S. McGilton, Manuel Montero‐Odasso, Natalie A. Phillips, Randi Pilon, Julie M. Robillard, Kenneth Rockwood, Jennifer Walker, Victor Whitehead

Bibliographic record

VenueNeurology · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicDelphi Technique in Research
Canadian institutionsLaurentian UniversityDalhousie UniversityUniversity of British ColumbiaWestern UniversityUniversity of TorontoUniversity of CalgaryMcGill UniversityDouglas CollegeUniversity of WaterlooToronto Rehabilitation InstituteLawson Health Research InstituteSunnybrook HospitalUniversity of AlbertaBaycrest Hospital
Fundersnot available
KeywordsHoganGerontologyDementiaLibrary scienceMedicineSociologyComputer scienceAnthropologyInternal medicine

Abstract

fetched live from OpenAlex

To assess the impact of establishing a national program for research on dementia in Canada.

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)0.079
metaresearch head score (Gemma)0.068
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.945
Threshold uncertainty score0.416

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0790.068
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0060.001
Scholarly communication0.0030.001
Open science0.0020.007
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.261
GPT teacher head0.521
Teacher spread0.260 · 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.

Study designObservational
DomainEvaluation
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

Citations1
Published2020
Admission routes2
Has abstractyes

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