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Record W4313346970 · doi:10.36740/wlek202211112

PRACTICAL ASPECTS AND RESULTS OF COGNITIVE THERAPY IN THE EARLY RECOVERY PERIOD OF ISCHEMIC STROKE

2022· article· en· W4313346970 on OpenAlexaboutno aff
Olga G. Morozova, Anna N. Kosheleva, Bogdan S. Fedak, Volodymyr I. Ponomaryov, Aleksandr A. Yaroshevskiy, Iryna M. Kravchenko, Inna V. Reminiak

Bibliographic record

VenueWiadomości Lekarskie · 2022
Typearticle
Languageen
FieldNeuroscience
TopicNeurological Disorders and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMontreal Cognitive AssessmentCognitionRehabilitationStroke (engine)Physical therapyMedicineIschemic strokeCognitive rehabilitation therapyPhysical medicine and rehabilitationCerebral infarctionCognitive Assessment SystemPsychologyCognitive impairmentPsychiatryIschemia

Abstract

fetched live from OpenAlex

OBJECTIVE: The aim: To determine the impact of cognitive training on the degree of cognitive functions recovery and quality of life in the early recovery period of ischemic stroke. PATIENTS AND METHODS: Materials and methods: 108 patients with cerebral infarction were examined outpatiently, follow-up from 1 to 3 months from the onset of the disease. Basic assessment methods: screening index of cognitive disorders according to the Montreal Cognitive Assessment Scale (MoCA), SF-36 questionnaire. RESULTS: Results and Conclusions: Comprehensive rehabilitation measures for the early recovery period of ischemic stroke achieve improvement of the cognitive sphere: a significant increase in the average score on the Montreal scale of cognitive functions assessment (MoCA scale) in both observation groups.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
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)0.0010.000

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 head0.047
GPT teacher head0.299
Teacher spread0.252 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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