Comparison of The Canary System and <scp>DIAGNO</scp>dent for the in vitro detection of caries under opaque dental sealants
Bibliographic record
Abstract
AIM: The aim of the present study was to investigate the ability of operators using The Canary System and DIAGNOdent to detect natural pit and fissure caries under four commonly-used opaque dental sealants. METHODS: Mixed sound and carious pits/fissures (N = 105) selected from 40 human teeth were randomly assigned (10 teeth/group) to one of four opaque sealant groups (Delton, Embrace WetBond, Helioseal F, UltraSeal XT Plus). Selected pits/fissures sites on occlusal surfaces were scanned with The Canary System and DIAGNOdent, sealed, re-scanned, and subjected to polarized light microscopy to confirm whether the scanned regions were sound or carious. Sensitivities and specificities for each detection method before and after sealant placement were calculated. RESULTS: The Canary System and DIAGNOdent were able to distinguish between sound and carious tissue beneath opaque sealants with an accuracy of 76% and 59%, respectively. CONCLUSIONS: The Canary System can serve as a clinical tool to aid dental professionals to detect and monitor the status of caries lesions and tooth structure underneath sealant. The increased likelihood of false-positive diagnoses with DIAGNOdent due to intrinsic auto-fluorescence of sealant filler and opacifying agents might limit its usefulness as an aid to detect caries underneath opaque sealants.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".