Reproducibility and Diagnostic Outcomes of Two Visual-Tactile Criteria Used by Dentists to Assess Caries Lesion Activity: A Cross-Over Study
Bibliographic record
Abstract
UNLABELLED: The aim of this study was to evaluate the reproducibility and differences in diagnostic outcomes by practicing dental clinicians previously inexperienced in using the Nyvad criteria and the ICDAS II criteria with the Lesion Activity Assessment system (ICDAS II + LAA). Four volunteer dentists were randomly allocated to one of two groups. Both groups of dentists examined the same voluntary sample (n = 140) of caries active young adults using Nyvad and ICDAS II + LAA criteria in different sequences. The first group used the Nyvad criteria during period 1, followed by ICDAS II + LAA during period 2; the second group did the examinations in the opposite sequence. Before the period 1 and 2 examinations, dentists from both groups were trained with the Nyvad or ICDAS II + LAA criteria, depending on the group to which they were assigned. Intra-examiner agreement for lesion severity was high for both diagnostic instruments (weighted kappa 0.62-0.80). For lesion activity the intra-examiner unweighted kappa values ranged from 0.31 to 0.61 for ICDAS II + LAA and from 0.36 to 0.51 for Nyvad. The mean number of active non-cavitated caries lesions was significantly higher for ICDAS II + LAA (6.14 ± 5.4) than for Nyvad (3.90 ± 3.9) (p < 0.001). Active cavitated/dentinal caries lesions were significantly higher for ICDAS II + LAA (4.14 ± 4.1) than for Nyvad (2.13 ± 3.1) (p < 0.001). Both the Nyvad and ICDAS II + LAA diagnostic criteria showed high reproducibility for lesion severity assessment. The mean number of active caries lesions among high caries risk subjects was significantly higher using the ICDAS II + LAA criteria, which may subsequently lead to more caries treatment. TRIAL REGISTRATION: ISRCTN65592532.
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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 | 0.019 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".