Oral manifestations of patients with systemic sclerosis: a meta-analysis for case-controlled studies
Bibliographic record
Abstract
BACKGROUND: Systemic sclerosis (SSc) is a multisystem rheumatic disease. Orofacial manifestations are commonly in SSc but maybe usually ignored and overshadowed by other systemic complications. Multiple comparative studies have been conducted to investigate the possible links between SSc and oral manifestations. The present study aimed to investigate the oral health status in patients with SSc. METHODS: Pubmed, Embase, Web of Science, and Scopus were searched up to July 2020. Following outcomes were evaluated: Probing depth (PD), Attachment loss (AL), Bleeding on probing (BOP), Number or percentage of Sites with PD ≥ 4 mm, Prevalence of periodontitis, Number of teeth, Decayed Teeth, Missing teeth, Filled teeth, DMFT index, and the interincisal distance. Newcastle-Ottawa Scale (NOS) were applied for quality assessment. The statistical analysis was processed using the software STATA. RESULTS: 11 eligible studies were included. The maximum interincisor distance was significantly restricted in SSc patients (SMD - 1.061; 95 %CI [- 1.546, - 0.576]; Z = 4.29, P = 0.000).The prevalence of Periodontitis (OR 7.007; 95 %CI [3.529, 13.915]; Z = 5.56, P = 0.000), PD (SMD 3.101; 95 %CI [1.374, 4.829]; Z = 3.52, P = 0.000), AL(SMD 2.584; 95 %CI [0.321, 4.846]; Z = 2.24, P = 0.025), sites with PD ≥ 4mm (SMD 2.071 ; 95 %CI [0.267, 3.875]; Z = 2.25, P = 0.024) and the number of decayed teeth (SMD, 0.186; 95 %CI [0.007, 0.365]; Z = 2.04, P = 0.041) were increased significantly in SSc population in comparison with the controls. CONCLUSIONS: SSc patients have limited mouth opening, higher periodontitis prevalence, and worse periodontal status, as well as an increased number of decayed teeth. Routinely oral hygiene instruction and initial periodontal treatment is recommended for SSc patients.
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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 | 0.021 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.045 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".