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Record W2002006631 · doi:10.1136/bcr-2013-008825

Recurrent non-aneurysmal subarachnoid haemorrhage in Takayasu arteritis: is the cause immunological or mechanical?

2013· article· en· W2002006631 on OpenAlexaff
Umar Shuaib, Mahesh Kate, Joanne Homik, Thomas Jerrakathil

Bibliographic record

VenueBMJ Case Reports · 2013
Typearticle
Languageen
FieldMedicine
TopicVasculitis and related conditions
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineSubarachnoid haemorrhageTakayasu arteritisAneurysmArteritisVasculitisTakayasu's arteritisNeurologyInternal medicineRadiologyDisease

Abstract

fetched live from OpenAlex

Aneurysmal subarachnoid haemorrhage (SAH) is rarely associated with Takayasu's arteritis (TA). The present report describes a 21-year-old woman with recurrent SAH and TA. In addition, she also had recurrent spells of postural weakness in the bilateral lower limb occurring at the same time. Sequential CT of the head and MRI showed bilateral cortical SAH. Vascular imaging with MR angiogram and CT angiogram showed bilateral subclavian arteries and left common carotid artery occlusion with multiple hypertrophied collaterals vessels in the neck. There was no evidence of aneurysms in the intracranial vasculature in the conventional angiogram. The CT angiogram of the aorta showed severe stenosis of the abdominal aorta above the renal arteries. The patient was treated with immunomodulatory therapy and had a favourable outcome without further recurrence at end of 1 year of follow-up. A review of the literature showed 21cases with aneurysmal SAH and three cases non-aneurysmal SAH in patients with TA have been reported. Various factors are responsible for the reorganisation of the intracranial of the arteries in patients with chronic vasculitis in the presence of extracranial stenosis and occlusion, which could possibly explain the SAH in absence of aneurysm in patients with TA.

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)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.030
GPT teacher head0.301
Teacher spread0.271 · 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 designCase report
Domainnot available
GenreEmpirical

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

Citations10
Published2013
Admission routes1
Has abstractyes

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