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Cognitive functions in patients with episodic and chronic migraine

2019· article· en· W2983563880 on OpenAlexaboutno aff
Н. В. Латышева, E. G. Filatova, D. V. Osipova, A B Danilov

Bibliographic record

VenueMedical alphabet · 2019
Typearticle
Languageen
FieldMedicine
TopicMigraine and Headache Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMigraineMontreal Cognitive AssessmentAnxietyDepression (economics)CognitionIctalMedicineAudiologyInternal medicinePsychologyPsychiatryClinical psychologyPediatricsPhysical therapyCognitive impairmentElectroencephalography

Abstract

fetched live from OpenAlex

Cognitive impairment (CN) is widespread in migraines, but very little has been studied. There is evidence that these violations are present in patients with episodic migraine (EM) during an attack and in the interictal period. The purpose of this work was to study the prevalence of objective CN in patients with EM and chronic migraine (CM) during the period of minimal or absent pain. Methods. The study involved 74 patients with CM and 42 patients with rare EM (no more than four days of headache per month) aged 18-59 years. Anxiety and depression were assessed using the Hospital Anxiety and Depression Scale. Evaluation of cognitive functions was performed using the Montreal MoCA cognitive function evaluation scale, the DSST digit test and the Ray test of the RAVLT hearing aural learning. Results. in the CM group, there was a decrease in the results of the DSST, MoCA tests and the RAVLT general memory index compared with patients with EM. in both groups, the value of the latter indicator did not reach the standard. in 38 % of patients with chronic respiratory infections, the result on the MoCA scale did not reach the lower limit of normal. The absence of interrelations between the results of tests for cognitive functions and the levels of anxiety and depression was revealed. Conclusion. in patients with EM and CM, objective CNs are observed. Preventive therapy of EM is needed to reduce the risk of chronicity and progression of CN.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.999

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)0.0020.000

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 head0.006
GPT teacher head0.245
Teacher spread0.239 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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