Headache and cognitive impairment in patients with chronic cerebral ischemia
Bibliographic record
Abstract
Objective. Analysis of the relationship between cognitive impairment (CI) and headache (HA) in patients with chronic cerebral ischemia (CCI). Material and methods. The study included 97 patients aged 50 to 85 years (65 females, 32 males) diagnosed with CCI. The test group consisted of 64 patients with HA, and the comparison group included 33 patients without HA. The presence and severity of CI were determined by the Montreal Cognitive Assessment (MoCA) Score, and the brain white matter hyperintensity (WMHI) was determined by MRI according to the Fazekas scale. Results. Based on the HA diagnostic criteria of the International Classification of Headache Disorders, 3rd edition, primary forms of HA were diagnosed in 48 (75%) patients, tension headache (THA) prevailed (43.8%), migraine was diagnosed in 23.4%, and combined THA and migraine in 7.8%; secondary HA was detected in 16 (25%) patients. Moderate CI was more common in patients with CCI and HA than those without HA (48 vs. 18 subjects, respectively, p=0.041). In the test group, the mean MoCA total score was 19 [17, 24] vs. 23 [20, 26] in the comparison group (p=0.0001). Statistically significant differences were found when comparing the Fazekas score in WMHI groups (p=0.038). Conclusion. HA can be a risk factor for CCI progression and should be considered when developing therapeutic strategies.
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 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 | 0.001 | 0.002 |
| 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) | 0.001 | 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".