Bibliographic record
Abstract
Intracranial arterial dissecting diseases are rare and challenging diseases with a high associated morbidity and mortality. Their common pathomechanic origin is related to blood entering the vessel wall via an endothelial and intimal tear. Depending on the fate of the thus established intramural hematoma, different symptoms may ensue including mass effect, subarachnoid hemorrhage or ischemia. If the mural hematoma ruptures all vascular layers of the intradural artery, a subarachnoid hemorrhagic will occur. If the intramural hematoma reopens distally into the parent vessel on the other hand, ischemic embolic events may happen following intramural clot formation. If the mural hematoma does neither open itself into the parent vessel nor into the subarachnoid space, the vessel wall may dilate leading to occlusion of perforator branches and local ischemia. Organization of the mural hematoma may result in a chronic dissecting process which may eventually lead to formation of a "giant partially thrombosed" aneurysm with thrombus of varying ages within the vessel wall, ingrowth of vasa vasorum and recurrent dissections with subsequent growth of the aneurysm from the periphery. Treatment strategies of these diseases should take the underlying pathomechanism into consideration and include, depending on the presentation medical treatment, parent vessel occlusion, flow reversal or diversion, surgical options or a combined treatment protocol.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".