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Record W3184773661 · doi:10.1177/2327857921101058

Can Cognitive Assessment Games Save Us From Cognitive Decline?

2021· article· en· W3184773661 on OpenAlex
Mark Chignell, J. Bruce Morton, Monika Kastner, J.S. Lee

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueProceedings of the International Symposium on Human Factors and Ergonomics in Health Care · 2021
Typearticle
Languageen
FieldMedicine
TopicClinical Reasoning and Diagnostic Skills
Canadian institutionsSchwartz/Reisman Emergency Medicine InstituteNorth York General HospitalUniversity of Toronto
Fundersnot available
KeywordsCognitionCognitive Assessment SystemCognitive declinePsychologyHealth careCognitive neuropsychologyApplied psychologyMedicineDementiaNeuropsychologyCognitive impairmentPsychiatryEconomicsEconomic growth

Abstract

fetched live from OpenAlex

Loss of cognitive potential is one of the greatest impediments to human wellbeing and productivity. Our healthcare system does a poor job of managing cognitive development and cognitive decline because it measures cognitive status relatively infrequently and in the limited times when cognitive measures are taken, the instruments used tend to be blunt. In the panel that we presented at HCS 2021 we examined the potential for cognitive assessment games to provide more frequent cognitive assessment. We reported on the use of a cognitive assessment game to screen for delirium risk in emergency patients, and the development of a suite of assessment games that can assess executive functions and other cognitive abilities in both young and old. We conclude with a discussion of knowledge translation and implementation science strategies for incorporating game-based cognitive assessment into healthcare practice.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.502

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.032
GPT teacher head0.364
Teacher spread0.331 · 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