MétaCan
Menu
← Back to cohort
Record W3141783402 · doi:10.1038/s41598-021-86615-2

Concurrent validity of the short version of Montreal Cognitive Assessment (MoCA) for patients with stroke

2021· article· en· W3141783402 on OpenAlexaboutno aff
Yali Feng, Jiaqi Zhang, Yi Zhou, Bo Chen, Ying Yin

Bibliographic record

VenueScientific Reports · 2021
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
FundersChongqing Medical University
KeywordsMontreal Cognitive AssessmentStroke (engine)CognitionMedicineReceiver operating characteristicProtocol (science)Concurrent validityCognitive impairmentPhysical medicine and rehabilitationPhysical therapyInternal medicinePsychiatryPsychometricsClinical psychologyPathology

Abstract

fetched live from OpenAlex

The aim of the present study was to examine the concurrent validity of 2 Chinese versions of the short version of the Montreal Cognitive Assessment (MoCA) in patients with stroke, i.e., MoCA 5-minute protocol and National Institute for Neurological Disorders and Stroke and Canadian Stroke Network (NINDS-CSN) 5-minute Protocol. A total of 54 patients and 27 healthy controls were enrolled in this study. In this study, the Neurobehavioural Cognitive Status Examination (NCSE) was used as an external criterion of cognitive impairment. We found that the 5-min protocol did not differ from the MoCA in differentiating patients with cognitive impairments from those without (area under the receiver operating characteristic curve, AUC, of 0.948 for the MoCA 5-min protocol v.s. 0.984 for MoCA, P = 0.097). These three assessments demonstrated equal performance in differentiating patients with stroke from controls. The Chinese version of the MoCA 5-min protocol can be used as a valid screening for patients with stroke.

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)0.005
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.008
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.018
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.024
GPT teacher head0.330
Teacher spread0.306 · 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 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

Citations27
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueScientific Reports→Same topicDementia and Cognitive Impairment Research→French-language works237,207→