Bibliographic record
Abstract
The purpose of this paper is to discuss both normal perivascular spaces (PVSs) and pathological giant perivascular spaces (GPVSs). The anatomy and physiology of normal PVSs, including important immunological and lymphatic roles, are described. Special attention is given to the Magnetic Resonance Imaging (MRI) findings of both normal and GPVSs. Furthermore, the clinical features and pathogenesis of GPVSs are explored, with special emphasis on the pathological implications of these lesions, and their relevance. It is important that symptomatic GPVSs not be mistaken for more devastating disease processes. When the lesions in question occur in a characteristic location along the path of a penetrating vessel, are isointense with cerebrospinal fluid on all MRI sequences, do not enhance with contrast material, are not calcified, and have normal adjacent brain parenchyma, their appearance is pathognomonic of GPVSs. The clinician should realize that an extensive differential diagnosis is superfluous and that biopsy is unnecessary in these patients. Instead, the clinical focus should be aimed at neurosurgical intervention, as dictated by the symptoms of mass effect.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".