Clinical Performance of Two Fluorescence-Based Methods in Detecting Occlusal Caries Lesions in Primary Teeth
Bibliographic record
Abstract
This in vivo study aimed to evaluate the performance of 2 fluorescence-based methods in detecting occlusal caries lesions in primary teeth, compared with the performance of visual inspection and radiographic methods, and to propose a mathematic correction of the diagnostic parameters due to the imperfect reference standard method used in the study. Two examiners assessed the occlusal surfaces of 407 primary teeth (62 children) using visual inspection (ICDAS), radiographic, DIAGNOdent pen (pen type laser fluorescence; LFpen), and fluorescence camera (FC) methods. At the noncavitated threshold (NC) the reference standard method was the results of ICDAS, and at the dentine caries threshold (D3) teeth diagnosed with dentine caries by ICDAS or radiographic methods were subjected to operative treatment to confirm the presence of lesion. Reproducibility, sensitivity, specificity, accuracy, and the area under the ROC curve were calculated for the methods at both thresholds. At the NC threshold, LFpen had a slightly better performance compared to the FC and radiographic methods. However, at the D3 threshold, both fluorescence-based methods performed similarly. Visual inspection and radiographic methods presented higher specificities but lower sensitivities than fluorescence methods. After corrections, there was a significant decrease in some parameters. In conclusion, both fluorescence-based methods presented similar performance in detecting occlusal dentine caries lesions in primary teeth, but they usually gave more false-positive results than did the visual and radiographic methods. The correction proposed shows that the performance of the methods can be overestimated, and the correction should be validated and considered in further studies that use an imprecise reference standard method.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".