Oral Health Status in Marfan Syndrome: A Systematic Review and Meta-Analysis of 353 Cases
Bibliographic record
Abstract
This meta-analysis aimed to compare Marfan syndrome (MFS) patients with non-MFS populations based on orofacial health status to combine publicly available scientific information while also improving the validity of primary study findings. A comprehensive search was performed in the following databases: PubMed, Google Scholar, Scopus, Medline, and Web of Science, for articles published between 1 January 2000 and 17 February 2022. PRISMA guidelines were followed to carry out this systematic review. We used the PECO system to classify people with MFS based on whether or not they had distinctive oral health characteristics compared to the non-MFS population. The following are some examples of how PECO is used: P denotes someone who has MFS; E stands for a medical or genetic assessment of MFS; C stands for people who do not have MFS; and O stands for the orofacial characteristics of MFS. Using the Newcastle-Ottawa Quality Assessment Scale, independent reviewers assessed the articles' methodological quality and extracted data. Four case-control studies were analyzed for meta-analysis. Due to the wide range of variability, we were only able to include data from at least three previous studies. There was a statistically significant difference in bleeding on probing and pocket depth between MFS and non-MFS subjects. MFS patients are more prone to periodontal tissue inflammation due to the activity of FBN1 and MMPs. Early orthodontic treatment is beneficial for the correction of a narrow upper jaw and a high palate, as well as a skeletal class II with retrognathism of the lower jaw and crowding of teeth.
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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".