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Record W4386174806 · doi:10.46278/j.ncacn.20210804

Assessment of verbal and visual episodic memory, post-concussive complaints, and their relathionship following mild traumatic brain injury

2022· article· en· W4386174806 on OpenAlexaffvenue
Imene Kochbati, Hélène Audrit, Maude Laguë-Beauvais, Simon Tinawai, de Guise Elaine

Bibliographic record

VenueNeuropsychologie clinique et appliquée · 2022
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsMcGill UniversityMcGill University Health CentreUniversité de Montréal
Fundersnot available
KeywordsTraumatic brain injuryNeuropsychologyPsychologyEpisodic memoryAudiologyVerbal memoryVerbal learningPopulationVisual memoryNeuropsychological assessmentClinical psychologyPsychiatryCognitionMedicine

Abstract

fetched live from OpenAlex

Neuropsychological deficits following mild traumatic brain injury (mTBI) are usually discrete and sometimes difficult to detect. The aim of this study was to evaluate relationships between episodic memory and post-concussive symptoms (PCS). The sample was composed of 55 participants (aged 20 to 64 years), including 25 patients with mTBI and 30 healthy control participants. Participants completed the Rey Auditory Verbal Learning Test (RAVLT) and the Rey-Osterrieth Complex Figure (ROCF), and questionnaires measuring the intensity of memory complaints, fatigue, and sleep quality. Analysis of the data revealed: (a) no significant differences between both groups in episodic memory performance, in either the verbal or visual modalities (b) the intensity of PCS was significantly higher than expected in the normal population (no PCS symptoms), (c) no significant association was found between PCS and memory performance. These results suggest that, despite the fact that patients with mTBI complain of significant memory difficulties and PCS, the neuropsychological tests most commonly used in clinics, do not objectify the memory complaints.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.803
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.115
GPT teacher head0.446
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

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

Citations0
Published2022
Admission routes2
Has abstractyes

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