Nail gun injuries to the head with minimal neurological consequences: a case series
Bibliographic record
Abstract
BACKGROUND: An estimated 3700 individuals are seen annually in US emergency departments for nail gun-related injuries. Approximately 45 cases have been reported in the literature concerning nail gun injuries penetrating the cranium. These cases pose a challenge for the neurosurgeon because of the uniqueness of each case, the dynamics of high pressure nail gun injuries, and the surgical planning to remove the foreign body without further vascular injury or uncontrolled intracranial hemorrhage. CASE PRESENTATION: Here we present four cases of penetrating nail gun injuries with variable presentations. Case 1 is of a 33-year-old white man who sustained 10 nail gunshot injuries to his head. Case 2 is of a 51-year-old white man who sustained bi-temporal nail gun injuries to his head. Cases 3 and 4 are of two white men aged 22 years and 49 years with a single nail gun injury to the head. In the context of these individual cases and a review of similar cases in the literature we present surgical approaches and considerations in the management of nail gun injuries to the cranium. Case 1 presented with cranial nerve deficits, Case 2 required intubation for low Glasgow Coma Scale, while Cases 3 and 4 were neurologically intact on presentation. Three patients underwent angiography for assessment of vascular injury and all patients underwent surgical removal of foreign objects using a vice-grip. No neurological deficits were found in these patients on follow-up. CONCLUSIONS: Nail gun injuries can present with variable clinical status; mortality and morbidity is low for surgically managed isolated nail gun-related injuries to the head. The current case series describes the surgical use of a vice-grip for a good grip of the nail head and controlled extraction, and these patients appear to have a good postoperative prognosis with minimal neurological deficits postoperatively and on follow-up.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".