Comparative Accuracy Of Visual Examination And E-Dental Checkup (Teledentistry) In Dental Caries Detection Of Pediatric Population- A Systematic Review And Meta-analysis.
Bibliographic record
Abstract
Objective: To assess the comparative accuracy of visual examination and e-dental check-up (teledentistry) in dental caries detection of the pediatric population through a systematic review and meta-analysis. Methods: A comprehensive search of electronic databases, including PubMed, EMBASE, Scopus, and Web of Science, was conducted to identify relevant studies published with no time frame. Studies comparing the diagnostic accuracy of visual examination and teledentistry for dental caries detection in children were included. The Newcastle-Ottawa Scale was used to assess the risk of bias in the included studies. Data extraction and analysis were performed using meta-analysis software. Results: This systematic review and meta-analysis has been registered at the International Prospective Register Of Systematic review- PROSPERO- CRD42023452855. This review follows the guidelines of preferred reporting items in systematic review and meta-analysis (PRISMA) guidelines. A total of 3 studies met the inclusion criteria. The pooled sensitivity and specificity of teledentistry for dental caries detection were 90.3% (95% CI: 86.5%-93.2%) and 91.0% (95% CI: 87.7%-93.3%), respectively. The pooled accuracy of teledentistry was 92.1% (95% CI: 89.4%-94.2%). There was no significant difference in the diagnostic accuracy of teledentistry and visual examination. Conclusions: Teledentistry is a promising tool for dental caries detection in the pediatric population, demonstrating comparable accuracy to visual examination. Further research is needed to evaluate the long-term effectiveness and cost-effectiveness of teledentistry in pediatric dental care.
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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 | 0.031 | 0.084 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.053 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".