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The relationship between cognitive functioning and syndromic characteristics and neuroimaging changes in patients with different types of encephalopathies

2024· article· en· W4390935723 on OpenAlexaboutno aff
Khrystyna Duve, Олена Венгер

Bibliographic record

VenueINTERNATIONAL NEUROLOGICAL JOURNAL · 2024
Typearticle
Languageen
FieldMedicine
TopicAlcoholism and Thiamine Deficiency
Canadian institutionsnot available
Fundersnot available
KeywordsNeuroimagingMedicineCognitionTraumatic brain injuryCognitive skillPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

Cognitive impairment can be a consequence of direct and indirect brain injury, hypoxia, edema, metabolic disorders, neurodegenerative processes, metabolic encephalopathies, electrolyte abnormalities, organ failure, the effects of pesticides, toxins, drugs, and infectious processes. The results are presented from the study on correlations between cognitive functioning and syndromic characteristics and neuroimaging changes in patients with chronic post-traumatic (CTE), chronic vascular (CVE), chronic alcohol-induced (CAIE) and post-infectious (PIE) encephalopathies. The data of 520 medical records of patients with CTE (n = 145), CVE (n = 145), CAIE (n = 102) and PIE (n = 128) were analyzed. Neuroimaging was performed using multislice computed tomography. Cognitive functions were examined using the Montreal Cognitive Assessment. Statistical analysis of data was carried out with the help of computer software Microsoft Excel and Statistica 13.0. There was a probable relationship between cognitive functioning and extrapyramidal syndrome in patients with CVE; cognitive impairment and emotional lability disorder in patients with CAIE; cephalalgia syndrome, motor disorder syndrome and cerebellar ataxia syndrome in patients with PIE. In participants with CTE and CAIE, there was a significant correlation between cognitive functioning and ventricular enlargement; in patients with PIE — between cognitive functioning and the enlargement of the subarachnoid spaces.

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)0.000
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 score0.006
Threshold uncertainty score0.149

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.027
GPT teacher head0.273
Teacher spread0.246 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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Same venueINTERNATIONAL NEUROLOGICAL JOURNALSame topicAlcoholism and Thiamine DeficiencyFrench-language works237,207