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Record W4387078611 · doi:10.1136/bcr-2023-255702

Acute aortic intraluminal thrombus with embolisation and lower-limb ischaemia following intravenous iron sucrose infusion reaction

2023· article· en· W4387078611 on OpenAlexaff
Iva Okaj, Menaka Pai, John Harlock, Theodore E. Warkentin

Bibliographic record

VenueBMJ Case Reports · 2023
Typearticle
Languageen
FieldMedicine
TopicAortic aneurysm repair treatments
Canadian institutionsHamilton Health SciencesHamilton Regional Laboratory Medicine ProgramMcMaster University
Fundersnot available
KeywordsMedicineVomitingThrombusAnesthesiaChest painComplicationPulmonary embolismMyocardial infarctionInternal medicineCardiologySurgery

Abstract

fetched live from OpenAlex

A woman in her 50s developed iron deficiency anaemia. Her medical history included hypertension, asthma and remote postpartum pulmonary embolism. There was a strong family history of atherosclerosis. After receiving intravenous iron sucrose (500 mg), she developed vomiting and large-volume diarrhoea, followed by diaphoresis, back pain, haemoconcentration (haematocrit increase, 0.242 to 0.326), leucocytosis and platelet count decline. Myocardial infarction was ruled out and the truncal pain subsided. However, 2 days postdischarge, she was diagnosed with aortic intraluminal thrombus (ILT) with embolisation into the lower extremities. The limbs were salvaged by emergency embolectomies and fasciotomies. Acute aortic ILT is a rare disorder that has not been previously reported as a complication of parenteral iron therapy. We postulate that acute intravascular volume losses (vomiting and diarrhoea) with resulting haemoconcentration and catecholamine-associated platelet activation and consumption, in a patient with subclinical aortic atherosclerosis, triggered acute aortic ILT presenting as lower-limb ischaemia.

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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0050.004
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 head0.015
GPT teacher head0.301
Teacher spread0.286 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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