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Record W2768519360 · doi:10.1097/wad.0000000000000219

Computerized Cognitive Testing in Primary Care

2017· article· en· W2768519360 on OpenAlexaff
Geneva Millett, Gary Naglie, Ross Upshur, Liisa Jaakkimainen, Jocelyn Charles, Mary C. Tierney

Bibliographic record

VenueAlzheimer Disease & Associated Disorders · 2017
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsCentre for Family MedicineUniversity of TorontoSunnybrook Health Science CentreSinai Health SystemHealth Sciences CentreLunenfeld-Tanenbaum Research InstituteBaycrest Hospital
Fundersnot available
KeywordsPrimary careDementiaCognitionMedicineCognitive testTest (biology)Cognitive impairmentGerontologyFamily medicinePsychiatryDisease

Abstract

fetched live from OpenAlex

Computerized cognitive assessment tools may facilitate early identification of dementia in the primary care setting. We investigated primary care physicians' (PCPs') views on advantages and disadvantages of computerized testing based on their experience with the Computer Assessment of Mild Cognitive Impairment (CAMCI). Over a 2-month period, 259 patients, 65 years and older, from the family practice of 13 PCPs completed the CAMCI. Twelve PCPs participated in an individual interview. Generally, PCPs felt that the relationship between them and their patients helped facilitate cognitive testing; however, they thought available paper tests were time consuming and not sufficiently informative. Despite concerns regarding elderly patients' computer literacy, PCPs noticed high completion rates and that their patients had generally positive experiences completing the CAMCI. PCPs appreciated the time-saving advantage of the CAMCI and the immediately generated report, but thought the report should be shortened to 1 page and that PCPs should receive training in its interpretation. Our results suggest that computerized cognitive tools such as the CAMCI can address PCPs' concerns with cognitive testing in their offices. Recommendations to improve the practicality of computerized testing in primary care were suggested.

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 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)0.001
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 score0.092
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.320
Teacher spread0.288 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations12
Published2017
Admission routes1
Has abstractyes

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