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Record W3209793086 · doi:10.1186/s12883-021-02419-x

Delayed onset of fat embolus in the cerebral venous system after breast and hip augmentation: a case report

2021· article· en· W3209793086 on OpenAlexaboutno aff
Wei Dong, Dingyuan Wan, Xiang Yang, Min Fu, Xing Liu, Hao Li, Xiaoqi Xie

Bibliographic record

VenueBMC Neurology · 2021
Typearticle
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineVenous thrombosisCerebral infarctionEmbolusSurgeryAnesthesiaThrombosisInternal medicineIschemia

Abstract

fetched live from OpenAlex

BACKGROUND: Cerebral fat embolism (CFE) is a subtype of fat embolism syndrome which tends to cause ischemic cerebral infarction. Fat embolism in the cerebral venous system have not been reported. We hereby present a rare case of fat embolus formed in the cerebral venous system 10 days after cosmetic surgery, and describe our management of this patient. CASE PRESENTATION: A 26-year-old woman with the disturbance of consciousness and recurrent convulsions of the right upper extremity over a 21-h period was admitted to our hospital. The patient was initially diagnosed with haemorrhagic infarction, and cerebral venous thrombosis (CVT) was suspected based on computed tomography (CT). A diagnosis of CFE was confirmed based on surgical findings. Breast and hip augmentation performed 10 days ago was considered the underlying cause. Drug-induced hypothermia, low molecular weight heparin, atorvastatin, dexamethasone, piperacillin/tazobactam, valproic acid, and mannitol were applied. On hospital day 30, she was discharged with a Montreal Cognitive Assessment score of 25. CONCLUSIONS: Fat embolism can occur in the cerebral venous system, and may mimic CVT symptoms rather than CFE symptoms. Early identification of the nature of the embolus is essential. The use of heparin may prevent secondary thrombus formation, and accelerate fat embolus decomposition.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.326
Threshold uncertainty score0.314

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.016
GPT teacher head0.262
Teacher spread0.247 · 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.

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

Citations4
Published2021
Admission routes1
Has abstractyes

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