MétaCan
Menu
Back to cohort
Record W4220667742 · doi:10.1186/s12883-022-02620-6

Boston cognitive assessment (BOCA) — a comprehensive self-administered smartphone- and computer-based at-home test for longitudinal tracking of cognitive performance

2022· article· en· W4220667742 on OpenAlexaboutno aff
Andrey Vyshedskiy, Rebecca Netson, Elisabeth Fridberg, Priyanka Jagadeesan, Matthew Arnold, Sophie Barnett, Anjali Gondalia, Victoria Maslova, Lauren de Torres, Simone Ostrovsky, Danijel Durakovic, Andrei Savchenko, Sienna McNett, Mikhail Kogan, Irene Piryatinsky, Dov Gold

Bibliographic record

VenueBMC Neurology · 2022
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
Fundersnot available
KeywordsCognitionMontreal Cognitive AssessmentRecallCronbach's alphaMedicineMemory spanCognitive testTest (biology)AudiologyPsychologyClinical psychologyWorking memoryPsychiatryPsychometricsCognitive impairmentCognitive psychology

Abstract

fetched live from OpenAlex

Longitudinal cognitive testing is essential for developing novel preventive interventions for dementia and Alzheimer's disease; however, the few available tools have significant practice effect and depend on an external evaluator. We developed a self-administered 10-min at-home test intended for longitudinal cognitive monitoring, Boston Cognitive Assessment or BOCA. The goal of this project was to validate BOCA. BOCA uses randomly selected non-repeating tasks to minimize practice effects. BOCA evaluates eight cognitive domains: 1) Memory/Immediate Recall, 2) Combinatorial Language Comprehension/Prefrontal Synthesis, 3) Visuospatial Reasoning/Mental rotation, 4) Executive function/Clock Test, 5) Attention, 6) Mental math, 7) Orientation, and 8) Memory/Delayed Recall. BOCA was administered to patients with cognitive impairment (n = 50) and age- and education-matched controls (n = 50). Test scores were significantly different between patients and controls (p < 0.001) suggesting good discriminative ability. The Cronbach's alpha was 0.87 implying good internal consistency. BOCA demonstrated strong correlation with Montreal Cognitive Assessment (MoCA) (R = 0.90, p < 0.001). The study revealed strong (R = 0.94, p < 0.001) test-retest reliability of the total BOCA score one week after participants' initial administration. The practice effect tested by daily BOCA administration over 10 days was insignificant (β = 0.03, p = 0.68). The effect of the screen size tested by BOCA administration on a large computer screen and re-administration of the BOCA to the same participant on a smartphone was insignificant (β = 0.82, p = 0.17; positive β indicates greater score on a smartphone). BOCA has the potential to reduce the cost and improve the quality of longitudinal cognitive tracking essential for testing novel interventions designed to reduce or reverse cognitive aging. BOCA is available online gratis at www.bocatest.org .

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.049
GPT teacher head0.339
Teacher spread0.290 · 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 designBench or experimental
Domainnot available
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

Citations26
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueBMC NeurologySame topicDementia and Cognitive Impairment ResearchFrench-language works237,207