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Record W2767928955 · doi:10.1177/1747493017741384

Cerebral amyloid angiopathy, cerebral microbleeds and implications for anticoagulation decisions: The need for a balanced approach

2017· article· en· W2767928955 on OpenAlexaff
Andreas Charidimou, Ashkan Shoamanesh, Rustam Al‐Shahi Salman, Charlotte Cordonnier, Luke A. Perry, Kevin N. Sheth, Alessandro Biffi, Jonathan Rosand, Anand Viswanathan

Bibliographic record

VenueInternational Journal of Stroke · 2017
Typearticle
Languageen
FieldMedicine
TopicIntracerebral and Subarachnoid Hemorrhage Research
Canadian institutionsMcMaster UniversityPopulation Health Research Institute
FundersBritish Heart Foundation
KeywordsCerebral amyloid angiopathyMedicineIntracerebral hemorrhageIntensive care medicineAtrial fibrillationClinical trialStroke (engine)CardiologyDiseaseInternal medicineDementiaSubarachnoid hemorrhage

Abstract

fetched live from OpenAlex

Cerebral amyloid angiopathy is a common hemorrhagic small vessel disease of the brain, often associated with high risk of spontaneous lobar intracerebral hemorrhage. When the suspicion of cerebral amyloid angiopathy is raised, clinicians are hesitant in prescribing oral anticoagulation in patients in whom it is otherwise indicated, including the case of non-valvular atrial fibrillation. This is one of the thorniest clinical dilemmas in the field currently. In this short Leading Opinion piece by an international panel of clinicians-researchers active in the field, we present our consistent approach and future outlook on oral anticoagulation post intracerebral hemorrhage and in the setting of clinical-radiologic evidence of cerebral amyloid angiopathy. We discuss recent advances and support a more balanced approach with implications for the wider neurological clinical community in regards to successful recruiting this patient population in ongoing and future randomized trials.

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 imitation

Not 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.

metaresearch head score (Codex)0.085
metaresearch head score (Gemma)0.178
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.085
Threshold uncertainty score0.450

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0850.178
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.001
Bibliometrics0.0030.002
Science and technology studies0.0040.007
Scholarly communication0.0160.022
Open science0.0030.006
Research integrity0.0120.023
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.045
GPT teacher head0.347
Teacher spread0.303 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations40
Published2017
Admission routes1
Has abstractyes

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