Efficacy of CBCT in Detecting Maxillary Sinus Mucosal Thickening and Periapical Pathology in Patients with Odontogenic Anomaly – A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Abstract Introduction: Odontogenic infection leads to increased thickness of maxillary sinus membrane. This is aided by close relationship between the roots of upper posterior teeth and floor of the maxillary sinus. This can be viewed by using imaging modalities. Aim: The review has been conducted to compare CBCT (Cone Beam Computed Tomography) with Orthopantomogram (OPG) in detecting maxillary sinus membrane thickening in patients with odontogenic anomaly through a meta-analysis. Materials and Methods: The review was carried out by following Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines and was registered as PROSPERO 2023 CRD42023390257. PubMed, Google Scholar and EBSCO host were queried for literature published from January 2000 to December 2022. Newcastle Ottawa scale evaluated the quality of articles. Results: Statistical analysis was performed and forest plot was plotted using RevMan (Review Manager) software version 5.3. Five studies were eligible and two of them were suitable for Meta-Analysis. The Standardized Mean Difference yielded a pooled estimate of 0.39 and preferred CBCT using a random effect model with a P value of 0.02 and an I 2 (heterogeneity) value of 59%. Funnel plot showed absence of publication bias. Conclusion: It was concluded that CBCT was effective, as compared to OPG in detecting maxillary sinus membrane thickening with odontogenic anomaly.
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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 | 0.015 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.036 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".