Cerebral artery myogenic reactivity: The next frontier in developing effective interventions for subarachnoid hemorrhage
Bibliographic record
Abstract
Aneurysmal subarachnoid hemorrhage (SAH) is a devastating cerebral event that kills or debilitates the majority of those afflicted. The blood that spills into the subarachnoid space stimulates profound cerebral artery vasoconstriction and consequently, cerebral ischemia. Thus, once the initial bleeding in SAH is appropriately managed, the clinical focus shifts to maintaining/improving cerebral perfusion. However, current therapeutic interventions largely fail to improve clinical outcome, because they do not effectively restore normal cerebral artery function. This review discusses emerging evidence that perturbed cerebrovascular "myogenic reactivity," a crucial microvascular process that potently dictates cerebral perfusion, is the critical element underlying cerebral ischemia in SAH. In fact, the myogenic mechanism could be the reason why many therapeutic interventions, including "Triple H" therapy, fail to deliver benefit to patients. Understanding the molecular basis for myogenic reactivity changes in SAH holds the key to develop more effective therapeutic interventions; indeed, promising recent advancements fuel optimism that vascular dysfunction in SAH can be corrected to improve outcome.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".