Effect of Placing a Clear Sealant on the Validity and Reproducibility of Occlusal Caries Detection by a Laser Fluorescence Device: An in vitro Study
Bibliographic record
Abstract
The principal aim of this in vitro study was to assess the validity and reproducibility of the detection of occlusal caries using laser fluorescence (LF), prior to and following the placement of a clear fissure sealant. It also aimed to compare the manufacturer's standard cut-off recommendations with those published for in vitro studies and to compare the validity and reproducibility of LF with clinical visual examination (CVE) for the detection of occlusal caries under sealants. Three clinicians independently examined visually and with LF 37 extracted teeth (25 molars, 12 premolars), with a range of clinical caries from apparently sound to cavitated dentinal caries. Examinations were conducted under dental surgery conditions. Subsequently, the teeth were serially sectioned to provide the validating criterion. Following placement of the sealant, the specificity generally increased but there was an associated loss of sensitivity, at both the D1 (enamel and dentine) and D3 (dentine) diagnostic thresholds. The LF readings were significantly lower after placement of the sealant (p<0.05). The manufacturer's recommended cut-offs appear to be the most appropriate to use. The CVE had superior validity and reproducibility when compared to LF. Overall, the placement of a clear sealant does influence the detection of caries by LF but does not prevent the detection of caries by this 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 | 0.015 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".