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Record W4283728067 · doi:10.3390/medicina58070870

Cognitive Dysfunction in Migraineurs

2022· article· en· W4283728067 on OpenAlexaboutno aff
Tong Qin, Chun-Fu Chen

Bibliographic record

VenueMedicina · 2022
Typearticle
Languageen
FieldMedicine
TopicMigraine and Headache Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCognitionPsychologyMedicineNeuroscience

Abstract

fetched live from OpenAlex

Background and Objectives: Migraines are one of the most common types of primary headaches in neurology. Many studies to date have investigated cognitive impairment in migraineurs, but the results are inconsistent. This study aimed to investigate the cognitive function of migraineurs and explore the influencing factors. Material and Methods: A total of 117 patients with primary headaches (87 with migraine and 30 with tension-type headache (TTH)) and 30 healthy controls were enrolled. General information and data on headache clinical characteristics, and assessments of headache-related disability, psychological symptoms, and cognitive function were collected for statistical analysis. Results: The Montreal Cognitive Assessment (MoCA) total score and the scores of visuospatial and executive functions, language, and delayed recall in the migraine and TTH groups were significantly lower than those in the healthy control group (all p < 0.05). The MoCA total score did not correlate with Headache impact test-6, Migraine Disability Assessment Questionnaire, Patient Health Questionnaire-9, or Generalized Anxiety Disorder Questionnaire-7 scores in migraineurs (all p > 0.0125). The multiple linear regression analysis showed that age and duration of attack had a major influence on the overall and various fields of cognition in migraineurs. Conclusion: The study confirmed the impairment of cognitive function in patients with migraine and TTH, and found that the duration of attack had an effect on cognitive function in migraineurs.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.283
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.029
GPT teacher head0.302
Teacher spread0.273 · 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

Citations10
Published2022
Admission routes1
Has abstractyes

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