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Record W2945203120 · doi:10.1136/bcr-2018-227450

A case of antiphospholipid syndrome presenting cryptogenically as Budd-Chiari syndrome, then fulminantly as Libman-Sacks endocarditis

2019· article· en· W2945203120 on OpenAlexaff
Hart A Goldhar, Paloma O’Meara, Lana A. Castellucci

Bibliographic record

VenueBMJ Case Reports · 2019
Typearticle
Languageen
FieldMedicine
TopicSystemic Lupus Erythematosus Research
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsMedicineAntiphospholipid syndromeEndocarditisStroke (engine)Inferior vena cavaThrombophiliaPopulationInternal medicineCardiologySurgeryThrombosis

Abstract

fetched live from OpenAlex

A 58 year-old left-handed woman was transferred to our hospital with an evolving left middle cerebral artery stroke, severe thrombocytopenia and elevated inflammatory markers. She had a history of chronic Budd-Chiari syndrome (BCS) 16 months prior, attributed to a calcified web in the inferior vena cava that was stented. No thrombophilia testing was performed at that time. The current presentation demonstrated dense right-sided facial and arm paresis and neglect. Erythrocyte sedimentation rate and C-reactive protein were elevated, an autoimmune workup was consistent with a new diagnosis of systemic lupus erythematosus and triple-positive antiphospholipid antibodies. A transesophageal echocardiogram demonstrated a vegetation consistent with Libman-Sacks endocarditis (LSE), thought to have embolised to the brain. The patient was treated acutely with steroids, intravenous immunoglobulin and clopidogrel. This case demonstrates an atypical constellation of the antiphospholipid syndrome, with a novel presentation of BCS and LSE, and reinforces the importance of hypercoagulability screening in this population.

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)0.000
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 score0.004
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.005
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0020.002
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0020.001

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 head0.021
GPT teacher head0.325
Teacher spread0.304 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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