Penetrating intracranial injury from a pencil in a pediatric patient: illustrative case
Bibliographic record
Abstract
BACKGROUND: Intracranial penetrating injuries from a pencil are exceptionally rare. The most common mechanism is a child running while holding a pencil. Potential consequences of intracranial pencil injury include direct trauma to brain structures, vascular injury, and intracranial abscess formation. OBSERVATIONS: A 3-year-old girl was at daycare and had fallen while running with a pencil. Computed tomography showed a pencil penetrating the left parietal bone through the left temporal lobe, terminating in the posterior limb of the internal capsule. Cerebral angiography was performed prior to the removal of the pencil to rule out vascular injury. Angiography of the left carotid artery revealed slight irregularity in the left M2 but no active extravasation. The patient was then taken to the operating room to have the pencil removed. Postoperatively, she did well and was discharged home after 6 days with no neurological deficits. LESSONS: Pencils are rare causes of intracranial injury in children. Definitive vascular imaging prior to pencil removal to rule out vascular injury and minimize the risk of hemorrhage after removal is recommended. Intraoperative irrigation and debridement, followed by antibiotics, are recommended to avoid abscess formation. Follow-up vascular imaging is recommended to rule out pseudoaneurysm. https://thejns.org/doi/10.3171/CASE24494.
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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) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.005 |
| 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".