Memory and attention deficit in chronic migraine
Bibliographic record
Abstract
Background. Memory and attention deficits are prevalent in the chronic pain population. There are multiple common mechanisms in chronic pain and cognitive impairment. However, the presence, prevalence and clinical burden of such impairment are frequently underestimated.Objective: to evaluate subjective and objective cognitive deficits in patients with chronic migraine (CM).Materials and methods. We recruited 53 subjects with CM and 22 genderand age-matched controls with low-frequency episodic migraine (a maximum of 4 headache days per month) aged 18–59. All patients filled in the HADS (Hospital Anxiety and Depression Scale) anxiety and depression scale and Pittsburg Sleep Quality Inventory (PSQI). Cognitive function was assessed with Montreal Cognitive Assessment (MoCA), Digital Symbol Substitution Test (DSST), Rey Auditory Verbal Learning Test (RAVLT) and the Perceived Deficits Questionnaire (PDQ-20).Results. 56 % of patients with CM complained of memory problems. Decreased cognitive function was also observed during self-assessment using the PDQ-20 questionnaire. Objectively, we found a significant decrease in 90-second DSST results and RAVLT total recall and learning rates. 44 % of subjects with CM scored lower than 26 points on MoCA. Most frequently we found impairments in attention (75 %), memory/delayed recall (50 %), language (50 %) and executive function (37 %). Depression and sleep quality correlated with only several parameters of cognitive tests.Conclusion. Subjective and objective cognitive deficits are prevalent in the CM population. Most often memory and attention are impaired. Cognitive complaints need to be carefully assessed, and treatment of such impairment may improve quality of life and decrease disability in CM.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".