Traffic accidents, maxillofacial injuries and risk factors: A systematic review of observational studies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it