P.110 Systematic review of civilian pediatric intracranial gunshot wounds
Bibliographic record
Abstract
Background: Pediatric craniocerebral gunshot wounds occur in the context of both accidental and intentional trauma. Unique physiologic factors merited research into prognostic factors and treatment priorities in the pediatric population. Methods: A systematic search of MEDLINE, EMBASE, Web of Science, LILACS, Cochrane Registered Trials and Systematic Reviews, ISRCTN, and ClinicalTrials.gov was conducted. Selection criteria included all studies published in any language since 2000 which described intracranial isolated gunshot wounds in a civilian individual or population of pediatric age. Post-mortem and epidemiological studies were excluded. Screening was conducted through Covidence. Results: Initial database search revealed 349 unique studies for abstract and title screening. Fifty studies were selected for full text screening. Nine studies were included in the final review. Study quality was assessed with the Newcastle-Ottawa Scale. Case series noted bullet migration, pituitary deficiency, neurovascular and neuropsychological concerns. Three single-center retrospective studies of 71, 30, and 48 pediatric patients suggested multiple negative prognostic signs on initial presentation. Early aggressive surgical treatment was recommended by some authors. Conclusions: This systematic review analyzed the best current understanding of evidence for prognostic factors and treatment considerations of intracranial gunshot wounds in the pediatric neurotrauma context. Areas for future research with larger multi-center studies were highlighted.
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 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 | 0.008 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.012 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".