Aneurysmal subarachnoid haemorrhage from a neuroimaging perspective
Bibliographic record
Abstract
Neuroimaging is a key element in the management of patients suffering from subarachnoid haemorrhage (SAH). In this article, we review the current literature to provide a summary of the existing neuroimaging methods available in clinical practice. Noncontrast computed tomography is highly sensitive in detecting subarachnoid blood, especially within 6 hours of haemorrhage. However, lumbar puncture should follow a negative noncontrast computed tomography scan in patients with symptoms suspicious of SAH. Computed tomography angiography is slowly replacing digital subtraction angiography as the first-line technique for the diagnosis and treatment planning of cerebral aneurysms, but digital subtraction angiography is still required in patients with diffuse SAH and negative initial computed tomography angiography. Delayed cerebral ischaemia is a common and serious complication after SAH. The modern concept of delayed cerebral ischaemia monitoring is shifting from modalities that measure vessel diameter to techniques focusing on brain perfusion. Lastly, evolving modalities applied to assess cerebral physiological, functional and cognitive sequelae after SAH, such as functional magnetic resonance imaging or positron emission tomography, are discussed. These new techniques may have the advantage over structural modalities due to their ability to assess brain physiology and function in real time. However, their use remains mainly experimental and the literature supporting their practice is still scarce.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".