On the sensitivity of thermophotonic lock-in imaging and polarized Raman spectroscopy to early dental caries diagnosis
Bibliographic record
Abstract
Dental caries is the leading cause of tooth loss, which can promptly be prevented if detected in early stages of progression. Unfortunately, conventional diagnostic modalities currently used in dentistry lack the sensitivity to detect early caries. The authors' intention is to compare the ability of polarized Raman spectroscopy and thermophotonic imaging to make early caries diagnosis. Extracted human teeth with no visible stain or defects were artificially demineralized in accordance to a well-known protocol in dentistry for simulated early caries development at several demineralization stages. Samples were then inspected using polarized Raman spectroscopy and thermophotonic imaging. The sensitivities of these two diagnostic modalities are compared, and the results are verified using transverse micro-radiography. It was found that compared to polarized Raman spectroscopy, thermophotonic imaging exhibits superior sensitivity to very early stages of demineralization.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".