In vivo validity of proximal caries detection in primary teeth, with histological validation
Bibliographic record
Abstract
Background Detection and diagnosis of proximal caries in primary molars are challenging. Aim The aim of this in vivo study was to assess the validity and reproducibility of four methods of proximal caries detection in primary molar teeth. Design Eighty‐two children (5‐10 years) were recruited. Initially, 1030 proximal surfaces were examined using meticulous visual examination (ICDAS) (VE1), bitewing radiographs (RE), and a laser fluorescence pen device (LF1). Temporary tooth separation (TTS) was achieved for 447 surfaces, and these were re‐examined visually (VE2) and using the LF pen (LF2). Three hundred and fifty‐six teeth (542 surfaces) were subsequently extracted and provided histological validation. Results At D1 (enamel and dentine caries) diagnostic threshold, the sensitivity of VE1, RE, VE2, LF1, and LF2 examination was 0.52, 0.14, 0.75, 0.58, and 0.60 and the specificity values were 0.89, 0.97, 0.88, 0.85, and 0.77, respectively. At D3 (dentine caries) threshold, the sensitivity values were 0.42, 0.71, 0.49, 0.63, and 0.65, respectively, whereas specificity was 0.93 for VE1 and VE2, and 0.98, 0.87, and 0.88 for RE, LF1, and LF2 examinations, respectively. ROC analysis showed radiographic examination to be superior at D3. Conclusion Meticulous caries diagnosis (ICDAS) should be supported by radiographs for detection of dentinal proximal caries in primary molars.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".