Periodontal precision: diagnostic skills and confidence of dentists in Asian countries in applying the 2017 EFP/AAP periodontal disease classification- a cross-sectional pilot study
Bibliographic record
Abstract
BACKGROUND: The classification of periodontal disease published in 2017 by the European Federation of Periodontology (EFP) and American Academy of Periodontology (AAP), provides a framework for diagnosis and treatment. The aim of this study was to evaluate the diagnostic skills and self-perceived confidence of dentists and dental students based in Asian countries in the use of this classification. METHODS: A cross-sectional analytic study design was employed. An online questionnaire encompassing four periodontitis cases was used for data collection. A total of 500 participants were invited to provide a diagnosis and rate their confidence for each case. RESULTS: Responses were provided by 312 participants completed including 192 females and 120 males. Analysis of variance (ANOVA) showed a statistically significant difference in accuracy across cases by Professional Role (F (9,924) = 2.304, p = 0.005), and an overall difference on accuracy by Professional Role (F (1,308) = 2.304, p = 0.012). The diagnostic accuracy mean was highest for periodontics specialists (57.81 ± 49.78) followed by general dentists (50.00 ± 50.31), other dental specialists (45.00 ± 50.06); and dental students (25.00 ± 43.55). A statistically significant difference in confidence was noted across Age Groups, Gender, and Roles (F(1,291) = 6.356, p < 0.001; F(1,293) = 13.747, p < 0.001; F(1,291) = 8.731, p < 0.001 respectively). There was no statistically significant effect on confidence ratings by any interaction between Location and Case. CONCLUSION: The study shows the diagnostic accuracy and confidence was highest amongst periodontology specialists followed by general dentists and undergraduate students. Overall the participants showed suboptimal diagnostic accuracy and confidence.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".