Lead toxicity from retained bullet fragments: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Firearm injury remains a public health epidemic in the United States. A large proportion of individuals with gunshot wounds subsequently have retained bullet fragments (RBF). There are no standard medical guidelines regarding bullet removal and the full extent of the consequences of RBF remains unknown. OBJECTIVE: To determine whether there is an association among RBF, elevated blood lead levels (BLL) and lead toxicity in survivors of firearm injury 16 years and older. METHODS: PubMed, EMBASE, CINAHL, Scopus, Cochrane Library, and Sociological Abstracts electronic databases were searched for all randomized controlled trials, prospective and retrospective cohort, case-control and cross-sectional studies published in the English language between 1988 and 2018. Quality assessment and risk of bias was evaluated using the Newcastle Ottawa Scale. A meta-analysis was performed using a random-effects model. RESULTS: The search yielded 2,012 articles after removal of duplicates. Twelve were included after full article review. Eleven studies supported an association between elevated BLL and RBF. Bony fractures were associated with increased risk of elevated BLL in three studies. A positive relationship between BLL and the number of RBF was also shown in three studies, with one study demonstrating 25.6% increase in BLL for every natural-log increase in RBF (1-228, p < 0.01). Meta-analysis demonstrated BLL significantly higher in individuals with RBF as compared to controls (5.47 μg/dL, p < 0.01). CONCLUSION: Patients with bony fractures or multiple RBF, who are at higher risk of elevated BLL, should be monitored for BLL in intervals of 3 months within the first year of injury. For patients who return with BLL above 5 μg/dL, all efforts must be undertaken to remove fragments if there is no potential to worsen the injury. LEVEL OF EVIDENCE: Systematic review, Meta-analysis, level III.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.032 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".