DETERMINE THE PROPORTION OFMAXILLOFACIAL TRAUMA RESULTING FROMDIFFERENT ETIOLOGIES AMONG CHILDREN:SYSTEMATIC REVIEW AND META-ANALYSIS
Bibliographic record
Abstract
Today understanding of maxillofacial trauma etiology among children is controversial. According to importance of subject, the aim of present Systematic review and meta-analysis was Determine the proportion of maxillofacial trauma resulting from different etiologies among children. From the electronic databases, PubMed, Cochrane Library, Embase, ISI have been used to perform a systematic literature between 2010 and 2020. Therefore, a software program (Endnote X8) has been utilized for managing the electronic titles. The quality of the studies included was assessed using the Newcastle-Ottawa Scale. For Data extraction, two reviewers blind and independently extracted data from abstract and full text of studies that included. Forest plots have been evaluated with the use of a software program available in the market (i.e., Comprehensive Meta-Analysis Stata V16).A total of 1263 potentially relevant titles and abstracts were found during the electronic and manual search. Finally, a total of ten publications fulfilled the inclusion criteria required for this systematic review. Prevalence of full was 28.78% (823) and effect size (ES, 4.51 95% CI -3.26, 12.28 P= 0.26) among the 10 studies and Prevalence of sports 24.19% (650) and effect size (ES, 6.43 95% CI -61.03, 73.89 P= 0.85) among the 7 studies.This systematic review and meta-analysis showed the most important etiology of maxilla trauma was road traffic accidents, after which the fall was the most frequent.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".