Supplementary Material for: In vitro Detection of Occlusal Caries on Permanent Teeth by a Visual, Light-Induced Fluorescence and Photothermal Radiometry and Modulated Luminescence Methods
Bibliographic record
Abstract
Background: The paradigm shift towards the nonsurgical management of dental caries relies on the early detection of the disease. Detection of caries at an early stage is of unequivocal importance for early preventive intervention. Objective: The aim of this in vitro study is to evaluate the performance of a visual examination using the International Caries Detection and Assessment System (ICDAS) criteria, two quantitative light-induced fluorescence (QLF) systems - Inspektor™ Pro and QLF-D Biluminator™ 2 (Inspektor Research Systems B.V., Amsterdam, The Netherlands) - and a photothermal radiometry and modulated luminescence, The Canary System® (Quantum Dental Technologies, Toronto, Ont., Canada) on the detection of primary occlusal caries on permanent teeth. Methods: A total of 60 teeth with occlusal surface sites ranging from sound to noncavitated lesions (ICDAS 0-4) were assessed with each detection method twice in a random order. Histological validation was used to compare methods for sensitivity, specificity, percent correct, and the area under the receiver operating characteristic curve (AUC), at standard and optimum sound thresholds. Interexaminer agreement and intraexaminer repeatability were measured using intraclass correlation coefficients. Results: Interexaminer agreement ranged between 0.48 (The Canary System®) and 0.96 (QLF-D Biluminator™ 2). Intraexaminer repeatability ranges were 0.33-0.63 (The Canary System®) and 0.96-0.99 (QLF-D Biluminator™ 2). The sensitivity range was 0.75-0.96 while that of specificity was 0.43-0.89. The AUC were 0.79 (The Canary System®), 0.87 (ICDAS), 0.90 (Inspektor™ Pro), and 0.94 (QLF-D Biluminator™ 2). Conclusion: ICDAS had the best combination of sensitivity and specificity followed by QLF-D Biluminator™ 2 at optimum threshold.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.654 | 0.256 |
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".