The feasibility of ultrasonography for the measurement of periodontal and peri‐implant phenotype: A systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Screening ultrasonography was proposed for monitoring periodontal soft tissues in the early 1960s, owing to its nonionizing, real-time, and cost-effective properties. Studies have provided convincing preliminary evidence for the use of ultrasound (US) in implant dentistry. PURPOSE: To assess the feasibility of ultrasonography (US) for measuring the buccal thickness of periodontal and peri-implant tissues. The secondary objective was to evaluate the reliability of US measurements compared to classic techniques, such as CBCT and directly measurements. MATERIALS AND METHODS: An electronic literature search was conducted by three independent reviewers through February 2023. The inclusion criteria were articles investigating at least five patients/cadavers with US measurements in periodontal or peri-implant buccal tissues. Compliance with methodological reporting standards and risk of bias was assessed using EULAR and QUADAS-C tools, respectively. Random-effects meta-analysis was conducted, using Bland-Altman analysis. Certainty of the evidence was assessed using GRADE. RESULTS: The final selection included 12 studies examining 458 patients and 13 cadavers, with a total of 226 implants, 1958 teeth and 60 edentulous sites. The body of evidence was assessed as partially compliant with methodological reporting standards for US studies and had an unclear to high risk of bias. Meta-analysis of five comparative studies showed no evidence of clinically significant bias between US and direct measurements (very low certainty), and between US and CBCT (very low certainty) for soft-tissue thickness. Likewise, for bone thickness, there is no evidence of clinically significant bias between US and CBCT (low certainty). CONCLUSIONS: Compared to the CBCT and direct measurements, ultrasonography might be a reliable approach for monitoring on periodontal and peri-implant phenotype. However, there is uncertainty about estimates of the actual effect, so further standardized and larger sample size of clinical research is needed.
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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 | 0.025 | 0.060 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.032 |
| Bibliometrics | 0.007 | 0.009 |
| 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) | 0.003 | 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".