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Record W2895883185 · doi:10.1590/0004-282x20180085

Chronic migraine patients show cognitive impairment in an extended neuropsychological assessment

2018· article· en· W2895883185 on OpenAlexaboutno aff
Karen S. Ferreira, Caroliny Trevisan Teixeira, Carolina Cáfaro, Gabriela Z. Oliver, Gabriela L. P. Carvalho, Larissa A. S. D. Carvalho, Brenda G. Silva, Fernanda B. B. Haes, Marcelo Cedrinho Ciciarelli

Bibliographic record

VenueArquivos de Neuro-Psiquiatria · 2018
Typearticle
Languageen
FieldMedicine
TopicMigraine and Headache Studies
Canadian institutionsnot available
Fundersnot available
KeywordsNeuropsychologyCognitive impairmentMigraineNeuropsychological assessmentMedicineCognitionClinical psychologyPsychologyPhysical medicine and rehabilitationPsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVES: The objective of the present study was to assess the presence of cognitive deficits in patients with chronic migraine, and to assess the main factors that trigger cognitive disorders, such as comorbidities or the use of medications. METHODS: Chronic migraine and control groups were interviewed in a case-control study. The frequency and intensity of the headache, medication used and associated comorbidities were determined. All patients were submitted to an extended neuropsychological assessment. RESULTS: The chronic migraine group (n = 30) had a worse performance in the Montreal Cognitive Assessment Test (p = 0.00), Verbal Fluency (p = 0.00), Stroop (p = 0.00), Clock Drawing Test (p = 0.00), Digit Span (p = 0.00) and Matrix Reasoning (p = 0.01). After statistical adjustment by linear regression, migraine continued to be the only relevant factor in the poorer performance in the Montreal Cognitive Assessment, Verbal Fluency, Clock Drawing and Stroop tests. CONCLUSIONS: Patients with chronic migraine have cognitive deficits in multiple tasks, regardless of the presence of comorbidities or the use of medications.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.001
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 head0.028
GPT teacher head0.359
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

Citations48
Published2018
Admission routes1
Has abstractyes

Explore more

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