Bullet embolus to the heart: A case report and systematic review of the literature
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".