Possibilities of Application of Transcatheter Treatment of Vascular Dementia with Binswanger’s Disease
Bibliographic record
Abstract
INTRODUCTION: The research is dedicated to the main features of brain angioarchitectonics caused by the development of Binswanger’s disease (BD), as well as to the effectiveness of the method of transcatheter laser revascularization of cerebral vessels in the treatment of this disease.MATERIALS: We examined 23 patients with BD whose age ranged from 58 to 81, mean age 78, including 15 (65.22%) men and 8 (34.78%) women. The examination included Clinical Dementia Rate (CDR), Mini-Mental State Examination (MMSE), Index Bartels (IB), laboratory examination, scintigraphy (SG), rheoencephalography (REG), Computed Tomography (CT), Magnetic Resonance Imaging (MRI) and cerebral multi-gated angiography (MUGA).14 (60.87%) patients underwent transcatheter interventions - Test Group.9 (39.13%) patients had conservative treatment - Control Group.RESULTS: Test Group: favorable clinical result - 9 (64.29%) cases; adequate clinical result - 5 (35.71%) cases; comparatively adequate and comparatively positive clinical results were not attained in any case.Control Group: favorable and adequate clinical results were not achieved in any case; comparatively adequate clinical result was gained in 7 (77.78%) cases; comparatively positive clinical result - in 2 (22.23%) cases.CONCLUSIONS: The method of transcatheter laser revascularization of cerebral vessels furthers natural angiogenesis, induces collateral and capillary revascularization both in ischemic areas and in closely located tissues, thereby improving cerebral blood flow. At the same time, laser energy promotes the restoration of metabolic processes in neurons. It significantly distinguishes the proposed method from conservative treatment.
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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 | 0.000 |
| 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".