Investigating the diagnostic accuracy of the 2017 periodontal classification in Canada
Bibliographic record
Abstract
Objectives: The purpose of this study was to explore the differences in periodontal diagnoses made by dental students, dental hygiene students, and dental professionals in Canada. A secondary objective was to identify potential factors, including familiarity and confidence in the 2017 Periodontal Classification, that may impact the diagnostic accuracy of periodontal diagnoses. Methods: This study was an electronic survey-based prospective study containing five clinical cases in periodontics to provide a diagnosis followed by 10 demographic/background/additional questions, distributed to: Year Three and Year Four dental students at the University of British Columbia (UBC) and University of Toronto (UofT), Year Three and Year Four Dental Hygiene Degree Program (DHDP) students at UBC, Periodontics Residents (PR) at five of the six programs offered across Canada (UBC, UofT, University of Alberta (UofA), University of Manitoba (UofM), and Dalhousie University (DAL) and Periodontics instructors at UBC (UBCI) which includes Periodontists and Dental Hygienists. Results: All five dental schools responded to the survey, with 104 respondents. Factors such as training level, familiarity with the 2017 Periodontal Classification, institution of training, and confidence in using the 2017 Periodontal Classification were found to impact diagnostic accuracy. Conclusions: This study found that there are variations in periodontal diagnoses made amongst dental professionals and students in Canada based on the 2017 Periodontal Classification. Specific recommendations include strengthening education in this area to enhance students’ awareness and confidence in using the classification, and implementing consensus calibration activities for instructors to utilize the new classification more effectively.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".