MétaCan
Menu
Back to cohort
Record W2998490011 · doi:10.52386/neurona.v36i3.74

HUBUNGAN NILAI P300 DENGAN MOCA-INA PADA PASIEN DENGAN GANGGUAN KOGNITIF VASKULAR PASCASTROKE ISKEMIK

2020· article· en· W2998490011 on OpenAlexaboutno aff
Susilo Susilo, Yudy Goysal, Abdul Muís, Muhammad Akbar, Andi Kurnia Bintang, Burhanuddin Bahar

Bibliographic record

VenueMajalah Kedokteran Neurosains Perhimpunan Dokter Spesialis Saraf Indonesia · 2020
Typearticle
Languageen
FieldMedicine
TopicPublic Health and Nutrition
Canadian institutionsnot available
Fundersnot available
KeywordsMontreal Cognitive AssessmentCognitive impairmentIschemic strokeStroke (engine)CardiologyMedicineInternal medicineNeuropsychologyCorrelationCognitionPsychiatryIschemiaPhysics

Abstract

fetched live from OpenAlex

ASSOCIATION OF P300 VALUE WITH MOCA-INA IN VASCULAR COGNITIVE IMPAIRMENT POST-ISCHEMIC STROKE PATIENTSABSTRACTIntroduction: Stroke is a major threat in human life because it can cause disability and mortality. Cognitive impairment in early stroke is strong predictor for long term vascular cognitive impairment while neuropsychology method is superior than conventional method to diagnose cognitive impairment, especially P300.Aim: To identify the association between P300 values and MoCA-Ina in vascular cognitive impairment post ischemic stroke patients.Methods: It is a cross sectional design study for ischemic stroke patients who suffered from vascular cognitive impairment during April to June 2018 in Neurology Clinic of Dr. Wahidin Sudirohusodo Hospital, Makassar. The statistical analysis was performed by Pearson’s correlation test.Result: There were 20 samples, male (60%) and female (40%). The average MoCA-Ina score was 19.35±6.06; the average P300 latency in Fz, Cz, and Pz were 370.22±49.01ms, 360.78±38.27ms, and 361.02±44.45ms, respectively; the average P300 in Fz, Cz, and Pz amplitude were 6.09±3.10µV, 5.67±3.49µV, and 6.10±2.77µV, respectively. The Pearson’s showed that P300 latency had significantly correlation with MoCA-Ina score while no correlation between the P300 amplitude and MoCA-Ina.Discussion: There was correlation between P300 latency with MoCA-Ina in vascular cognitive impairment post ischemic stroke patients.Keywords: Ischemic stroke, MoCA-Ina, P300 value, vascular cognitive impairment.ABSTRAKPendahuluan: Stroke merupakan suatu ancaman terbesar di kehidupan manusia karena dapat menimbulkan kecacatan dan kematian. Gangguan kognitif pada awal stroke merupakan prediktor kuat untuk gangguan kognitif vaskular jangka panjang dan metode neuropsikologi lebih unggul daripada metode konvensional untuk mendiagnosis gangguan kognitif, terutama P300.Tujuan: Untuk mengetahui hubungan nilai P300 dengan MoCA-Ina pada pasien gangguan kognitif vaskular pascastroke iskemik.Metode: Desain studi potong lintang terhadap pasien stroke iskemik yang mengalami gangguan kognitif vaskular selama bulan April sampai Juni 2018 di Poliklinik Saraf RSUP Dr. Wahidin Sudirohusodo, Makassar. Data diolah menggunakan uji korelasi Pearson’s.Hasil: Didapatkan 20 orang sampel laki-laki (60%) dan perempuan (40%). Nilai MoCA-Ina rata-rata 19,35±6,06; hasil rata-rata latensi gelombang P300 di Fz, Cz, dan Pz masing-masing adalah 370,22±49,01, 360,78±38,27, dan 361,02±44,45; rata-rata tinggi amplitudo P300 di Fz masing-masing adalah 6,09±3,10, 5,67±3,49, dan 6,10±2,77. Hasil uji korelasi Pearson’s menunjukkan latensi P300 berkorelasi signifikan terhadap MoCA-Ina, sedangkan amplitudo P300 tidak.Pembahasan: Ada hubungan antara latensi gelombang P300 dengan MoCA-Ina pada pasien gangguan kognitif vaskular pascastroke iskemik.Kata kunci: Gangguan kognitif vaskular, MoCA-Ina, nilai P300, stroke iskemik

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0170.002

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 head0.031
GPT teacher head0.263
Teacher spread0.232 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueMajalah Kedokteran Neurosains Perhimpunan Dokter Spesialis Saraf IndonesiaSame topicPublic Health and NutritionFrench-language works237,207