Penetrating intracranial nail-gun injury to the middle cerebral artery: A successful primary repair
Bibliographic record
Abstract
BACKGROUND: Penetrating nail-gun injuries to the head are rare, however, the incidence has been gradually rising over the last decade. While there is a large volume of case reports in the literature, there are only a few incidences of cerebrovascular injury. We present a case of a patient with a nail-gun injury to the brain, which compromised the cerebral vasculature. In this article, we present the case, incidence, pathology, and a brief literature review of penetrating nail-gun injuries to highlight the principles of management pertaining to penetration of cerebrovascular structures. CASE DESCRIPTION: A 26-year-old male presented with a penetrating nail-gun injury to his head. There were no neurological deficits. Initial imaging revealed that the nail had penetrated the cranium and suggested the vasculature to be intact. However, due to the proximity of the nail to the circle of Willis the operative approach was tailored in anticipation of a vascular injury. Intraoperatively removal of the foreign body demonstrated a laceration to the M1 branch of the middle cerebral artery (MCA), which was successfully repaired. CONCLUSION: To our knowledge, this is the first reported case of a vascular arterial injury to the MCA from a nail-gun injury. It is imperative to have a high clinical suspicion for cerebrovascular compromise in penetrating nail-gun injuries even when conventional imaging suggests otherwise.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".