Methods used for caries detection and diagnosis in Ontario dental practices: a cross-sectional survey
Bibliographic record
Abstract
BACKGROUND: Early detection of caries is essential for applying non-surgical treatment procedures and preventing the formation of cavitated lesions leading to unnecessary removal of tooth structure. Understanding dentists' preferences for caries detection tools can inform stakeholders about their strategies and knowledge of contemporary, evidence-based caries management approaches. However, there is a lack of research exploring the detection methods of caries commonly used by dentists in Ontario, Canada. The objective of this study was to investigate the methods of caries detection and diagnosis preferred by dentists in Ontario. METHODS: A 21-item self-reported survey was mailed to one thousand Ontario dental practices in the Winter of 2022. Descriptive and bivariate data analysis were performed to determine the associations between: demographics and professional practice characteristics (explanatory variables), and methods for detecting and diagnosing dental caries (outcome variables) using SPSS Statistics 29.0. RESULTS: A total of 325 dentists (33%) responded to the survey, with 274 answering all of the questions completely. The highest proportion of respondents were 35-44 years of age (32.8%) and male (53.4%). More than half of the respondents reported using a dental explorer to assess primary occlusal caries (57.6%), secondary caries (57.1%), and cervical caries (57.5%). Likewise, 57.9% of the participants reported using dental radiographs to diagnose proximal caries. Among additional caries detection tools, digital radiography (89.8%) and traditional radiography (84.7%) were the most used methods/modalities, while cone beam computed tomography was the least (12.8%). Most study participants did not use any caries classification system (77.7%) or caries risk assessment tool (85.3%). CONCLUSIONS: Participants preferred conventional methods for caries detection, instead of contemporary visual-tactile caries lesions classification and/or caries risk assessment systems. These findings indicate a need for continuing dental education programs tailored to evidence-based caries management approaches.
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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 | 0.003 | 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 | 0.000 |
| 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".