Open reduction versus close treatment in management of children mandibular fracture: A systematic review
Bibliographic record
Abstract
Mandibular fractures in children are the most common facial bone injury, which is 39% of all fractures. Adequate treatment of mandible fractures was still debated to restore the best physiological and aesthetic outcome. This systematic review aims to compare open reduction and closed treatment outcomes in children with mandibular fractures based on evidence from the current study. Four electronic databases were used: PubMed, ScienceDirect, Directory of Open Access Journals (DOAJ), and Google Scholar. Studies included were randomized and non-randomized clinical studies written in English and published in the last 10 years (2014). Children patients under 18 years of age, of any sex, with any mandible fracture treated with any functional appliance. Data was collected using a standard form agreed upon by two independent reviewers. The risk of bias and quality were assessed using the Newcastle-Ottawa Quality Assessment Scale (NOS). Ten studies were selected for this systematic review, including 554 patients. Half of studies chosen had a high risk of bias, 4 were deemed to have a moderate risk of bias, and one had a low risk of bias. Comparing ORIF and close treatment, we get the incidence of complications versus cases respectively, 9/182 versus 5/372. The data collected, although there is still a lot of bias in this review. We support close treatment as the first line treatment for children’s mandible fractures because the minimal number of possible complications.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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, 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".