In vitro detection and quantification of enamel and root caries using infrared photothermal radiometry and modulated luminescence
Bibliographic record
Abstract
Artificially created demineralized and remineralized carious lesions on the root and enamel of human teeth were examined by photothermal radiometry (PTR) and modulated luminescence (LUM). Fourteen extracted human teeth were used and a lesion was created on a 1 mmx4 mm rectangular window, spanning root to enamel, using a lactic acid-based acidified gel to demineralize the tooth surface. The lesion was then exposed to a remineralization solution. Each sample was examined with PTR/LUM on the root and enamel before and after treatment at times from 1 to 10 (5 on root) days of demineralization and 2 to 10 days of remineralization. Ten-day (5 on root) demineralized samples were remineralized. After completing all the experiments, transverse microradiography (TMR) analysis was performed to compare and correlate the PTR/LUM signals to the depth of lesions and mineral losses. The PTR and LUM amplitudes and phases showed gradual and consistent changes with treatment time. In this study, TMR showed good correlation coefficients with PTR and LUM. It was also found that the length of the treatment time did not correlate very well to any technique, PTR/LUM or TMR, which implies a significant degree of inhomogeneity of the demireralization and remineralization rates in each and every tooth.
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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 | 0.001 | 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) | 0.001 | 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".