MétaCan
Menu
Back to cohort
Record W3109593807 · doi:10.17116/jnevro202012010122

The trial of the efficacy and safety of sequential therapy with Mexidol forte 250 in acute and early recovery stages of hemispheric ischemic stroke

2020· article· en· W3109593807 on OpenAlexaboutno aff
М. А. Лоскутников, М.А. Домашенко, T.M. Vakin, Irina A. Trushina, V. I. Konstantinov, O.S. Proskuryakova, E. P. Shchukina

Bibliographic record

VenueS S Korsakov Journal of Neurology and Psychiatry · 2020
Typearticle
Languageen
FieldNeuroscience
TopicNeurological Disorders and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMontreal Cognitive AssessmentClinical endpointModified Rankin ScaleIschemic strokeStroke (engine)Barthel indexAnesthesiaAcute strokeClinical trialTherapeutic effectQuality of life (healthcare)Physical therapyInternal medicineCognitive impairmentActivities of daily livingIschemiaDisease

Abstract

fetched live from OpenAlex

OBJECTIVE: To evaluate the efficacy and safety of sequential therapy with mexidol (solution for intravenous and intramuscular injections) and mexidol forte 250 (coated tablets) in acute and early recovery stages of hemispheric ischemic stroke. MATERIAL AND METHODS: The changes in scores on the modified Rankin Scale (mRs) (primary endpoint), the National Institute of Health Stroke Scale (NIHSS), the Bartel Index (BI), the Montreal Cognitive Assessment (MoCa), the Beck Depression Inventory (BDI), the EuroQol Quality of Lifes Scale ( EQ-5D) were assessed in the end of treatment (secondary endpoint). RESULTS AND CONCLUSION: Prolonged and sequential therapy with mexidol at the dose 500 mg daily during 14 days (saturation phase) and mexidol forte 250 at the dose of 250 mg three times a day during 60 days (maximum therapeutic effect) provides additional opportunities for a more complete recovery in acute and early recovery stages of hemispheric ischemic stroke (increases quality of life, improves movement and cognitive functions).

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 score0.283
Threshold uncertainty score0.277

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 head0.017
GPT teacher head0.253
Teacher spread0.235 · 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

Citations8
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueS S Korsakov Journal of Neurology and PsychiatrySame topicNeurological Disorders and TreatmentsFrench-language works237,207