Therapeutic management of acute intracerebral haemorrhage
Bibliographic record
Abstract
Intracerebral haemorrhage (ICH) is a stroke resulting from spontaneous rupture of an intracranial vessel and is associated with high early mortality and long-term morbidity rates. With the exception of dedicated stroke units or neurocritical care, no surgical or medical intervention has been proven to effectively improve outcome following ICH. Pharmacotherapeutic considerations include optimal blood pressure control and the choice of antihypertensive agents. Acute haematoma expansion represents the most obvious acute treatment target. The use of haemostatic agents may have a role in ICH management; although it appears improved patient selection may be required before the use of these agents can be demonstrated clinically. In patients with anticoagulant-associated ICH, a number of therapeutic agents may be used to urgently reverse the coagulopathy, although further clinical trials are required. Recurrent bleeding and future thrombo-embolic event rates in patients who require anticoagulation following ICH risks are difficult to determine accurately, although risk stratification data are emerging. This article reviews the pathophysiology, natural history and the evidence supporting present therapeutic management practices for ICH. The authors' practice based on best available evidence is provided.
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) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".