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Record W3166132260

Dementia prevention, intervention, and care: 2020 report of the Lancet Commission

2020· article· en· W3166132260 on OpenAlexaff
Gill Livingston, Jonathan Huntley, Andrew Sommerlad, David Ames, Clive Ballard, Sube Banerjee, Carol Brayne, Alistair Burns, Jiska Cohen‐Mansfield, Claudia Cooper, Sergi G. Costafreda, Amit Dias, Nick C. Fox, Laura N. Gitlin, Robert Howard, Helen C. Kales, Mika Kivimäki, Eric B. Larson, Adesola Ogunniyi, Vasiliki Orgeta, Karen Ritchie, Kenneth Rockwood, Elizabeth L Sampson, Quincy M. Samus, Lon S. Schneider, Geir Selbæk, Linda Teri, Naaheed Mukadam

Bibliographic record

VenueUCL Discovery (University College London) · 2020
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsDalhousie University
Fundersnot available
KeywordsDementiaMedicineGerontologyRisk factorPovertyIntervention (counseling)PsychiatryDisease
DOInot available

Abstract

fetched live from OpenAlex
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 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)0.049
metaresearch head score (Gemma)0.087
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: Review · Consensus signal: Review
Teacher disagreement score0.158
Threshold uncertainty score0.314

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0490.087
Meta-epidemiology (narrow)0.0040.003
Meta-epidemiology (broad)0.0070.008
Bibliometrics0.0040.005
Science and technology studies0.0010.001
Scholarly communication0.0080.004
Open science0.0060.005
Research integrity0.0210.011
Insufficient payload (model declined to judge)0.0140.006

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 head0.017
GPT teacher head0.261
Teacher spread0.244 · 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
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

Citations1
Published2020
Admission routes1
Has abstractno

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