MétaCan
Menu
Back to cohort
Record W2092235705 · doi:10.1517/14656566.8.18.3097

Therapeutic management of acute intracerebral haemorrhage

2007· review· en· W2092235705 on OpenAlexaff
Negar Asdaghi, Dulka Manawadu, Kenneth Butcher

Bibliographic record

VenueExpert Opinion on Pharmacotherapy · 2007
Typereview
Languageen
FieldMedicine
TopicIntracerebral and Subarachnoid Hemorrhage Research
Canadian institutionsHealth Sciences CentreUniversity of Alberta
Fundersnot available
KeywordsMedicineIntensive care medicineNeurointensive careStroke (engine)CoagulopathyIntracerebral hemorrhageNatural historySurgeryInternal medicineSubarachnoid hemorrhage

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.982
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.137
GPT teacher head0.489
Teacher spread0.351 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations19
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueExpert Opinion on PharmacotherapySame topicIntracerebral and Subarachnoid Hemorrhage ResearchFrench-language works237,207