Management of delayed cerebral ischemia after subarachnoid hemorrhage
Bibliographic record
Abstract
For patients who survive the initial bleeding event of a ruptured brain aneurysm, delayed cerebral ischemia (DCI) is one of the most important causes of mortality and poor neurological outcome. New insights in the last decade have led to an important paradigm shift in the understanding of DCI pathogenesis. Large-vessel cerebral vasospasm has been challenged as the sole causal mechanism; new hypotheses now focus on the early brain injury, microcirculatory dysfunction, impaired autoregulation, and spreading depolarization. Prevention of DCI primarily relies on nimodipine administration and optimization of blood volume and cardiac performance. Neurological monitoring is essential for early DCI detection and intervention. Serial clinical examination combined with intermittent transcranial Doppler ultrasonography and CT angiography (with or without perfusion) is the most commonly used monitoring paradigm, and usually suffices in good grade patients. By contrast, poor grade patients (WFNS grades 4 and 5) require more advanced monitoring because stupor and coma reduce sensitivity to the effects of ischemia. Greater reliance on CT perfusion imaging, continuous electroencephalography, and invasive brain multimodality monitoring are potential strategies to improve situational awareness as it relates to detecting DCI. Pharmacologically-induced hypertension combined with volume is the established first-line therapy for DCI; a good clinical response with reversal of the presenting deficit occurs in 70 % of patients. Medically refractory DCI, defined as failure to respond adequately to these measures, should trigger step-wise escalation of rescue therapy. Level 1 rescue therapy consists of cardiac output optimization, hemoglobin optimization, and endovascular intervention, including angioplasty and intra-arterial vasodilator infusion. In highly refractory cases, level 2 rescue therapies are also considered, none of which have been validated. This review provides an overview of current state-of-the-art care for DCI management.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".