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Montreal cognitive Assessment Score: A Screening Tool for Cognitive Function in Traumatic Brain Injury (TBI) Population

2020· article· en· W3089331807 on OpenAlexaboutno aff
Keshav Mishra, Devendra Purohit, Somnath Sharma

Bibliographic record

VenueJournal of Neurology and Neuromedicine · 2020
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury and Neurovascular Disturbances
Canadian institutionsnot available
Fundersnot available
KeywordsTraumatic brain injuryMontreal Cognitive AssessmentCognitionPopulationPsychologyCognitive Assessment SystemClinical psychologyMedicinePsychiatryCognitive impairment

Abstract

fetched live from OpenAlex

Cognitive impairment is a major cause of morbidity and impaired quality of life in traumatic brain Injury (TBI) patients.Assessment of cognitive function using classically designed scales is time and resource intensive undertaking which also requires expert neuropsychiatrist referral.Montreal cognitive assessment Score (MoCA) is a brief screening tool designed to assess various cognitive domains which has been found to be more sensitive than Mini Mental State Examination (MMSE) score both in Alzheimer disease and subsequently in TBI population.Applied on TBI population, it reliably detects cognitive impairment in mild TBI, compared to normal controls and also differentiates cognitive disturbances between mild and severe TBI but its ability to differentiate cognitive function between mild and moderate TBI is equivocal.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.052
GPT teacher head0.319
Teacher spread0.267 · 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

Citations5
Published2020
Admission routes1
Has abstractyes

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Same venueJournal of Neurology and NeuromedicineSame topicTraumatic Brain Injury and Neurovascular DisturbancesFrench-language works237,207