Bibliographic record
Abstract
Background: Cerebral artery stenosis is an important risk factor for ischemic strokes. This study aims to explore intracranial and extracranial artery stenosis in a large northeast Chinese cohort. Methods: We recruited 14793 outpatients and hospitalized patients to identify cerebral artery stenosis. Artery stenosis screening was done with transcranial Doppler (TCD) for intracranial arteries and carotid duplex sonography for extracranial arteries. Results: More intracranial than extracranial artery stenoses were identified (4255 versus 2809, i.e. 28.8% versus 19.0%, P<0.05). Similarly, mere intracranial stenosis was significantly more common than extracranial artery stenosis in this population (2632 versus 1186, i.e. 17.8% versus 8%, P<0.05). Among all identified intracranial arteries stenoses, the proportion of middle cerebral artery (MCA) stenosis was the highest. More intracranial than extracranial artery stenoses was seen within each age group, and rates of both increased with age. Intracranial and extracranial artery stenosis was more frequently identified in males than females. Conclusions: Incidence of cerebral artery stenosis in the population increases with age. Intracranial artery stenosis is more common than extracranial artery stenosis and the MCA stenosis accounted for the highest proportion, within each age group. More males suffer from intracranial or extracranial artery stenosis than females.
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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.071 | 0.025 |
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".