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Record W4416302998 · doi:10.1016/j.tcr.2025.101267

Bullet embolus to the heart: A case report and systematic review of the literature

2025· article· en· W4416302998 on OpenAlexaff
Jacob L. Stubbs, Rachel Livergant, Karan D’Souza, Richard Cook, Naisan Garraway

Bibliographic record

VenueTrauma Case Reports · 2025
Typearticle
Languageen
FieldMedicine
TopicTrauma Management and Diagnosis
Canadian institutionsVancouver General HospitalUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsIntracardiac injectionEmbolusVentricleGunshot woundEmbolism

Abstract

fetched live from OpenAlex

Gun violence is a significant contributor to morbidity and mortality worldwide. Intravascular and intracardiac bullet emboli are a rare but recognized phenomenon; however, the optimal management of intracardiac bullet emboli remains unclear. In this article, we report a unique case of a bullet embolus to the mitral annulus and a systematic review of the literature on intracardiac bullet emboli. Our case involves a patient who sustained multiple gunshot wounds, including an intracardiac bullet embolus to his left ventricle that was identified and managed non-operatively. Blood lead levels increased slightly with the bullet left in situ but did not reach toxic levels. In our systematic review, we identified 56 articles encompassing 61 cases of intracardiac bullet emboli. The majority of previous cases reported emboli to the right ventricle (78.7 %) or right atrium (18.0 %). Management strategies varied, with 54 % of cases managed surgically, 27.9 % managed non-operatively, and 16.4 % managed endovascularly. Serial measurement of blood lead levels was uncommon among previous cases but is important to consider in order to avoid long-term lead toxicity in patients with conservatively managed emboli. This unique case report and review of the literature highlights the diagnostic and management challenges associated with intracardiac bullet emboli.

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)0.001
metaresearch head score (Gemma)0.001
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: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.168
Threshold uncertainty score0.348

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.013
GPT teacher head0.289
Teacher spread0.277 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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