Traffic accidents, maxillofacial injuries and risk factors: A systematic review of observational studies
Bibliographic record
Abstract
AIM: This study aimed to evaluate the scientific evidence regarding the risk factors for maxillofacial injuries among victims of traffic accidents. METHOD: A systematic review of articles published until February 2017 was carried out in the following databases: PubMed, Web of Science, Scopus, and Cochrane Library. Studies were selected by two independent reviewers (ϰ = 0.841). The risk of bias in the selected studies was assessed using an adapted version of the Newcastle-Ottawa Scale for observational studies. RESULTS: A total of 2703 records were found, of which only three articles fulfilled the inclusion criteria and were analyzed, including 422 244 patients. The male/female ratio ranged from 3.4: 1 to 6: 1. All eligible studies performed the multivariate statistical analysis. Eleven risk factors for maxillofacial traumas were identified: victim's gender (P < 0.05), age group (P < 0.05), residence region (P < 0.05), impact characteristics (P < 0.05), increased net change in velocity due to collision (P < 0.05), increase in occupant's height (P < 0.05), nonuse of protective equipment (P < 0.05), type of accident (P < 0.05), time of occurrence (P < 0.05), lesion severity (P < 0.05), and occurrence of concomitant lesions (P < 0.05). CONCLUSION: The results suggest that sociodemographic characteristics, as well as those related to the collision patterns and circumstances of traffic accidents, may influence the occurrence of maxillofacial injuries. However, the results should be interpreted with caution due to the high heterogeneity among studies.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
| gpt | Meta-epidemiology (broad) Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
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 | 0.017 | 0.069 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.016 | 0.017 |
| 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) | 0.003 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".