A pilot study of dentists' assessment of caries detection and staging systems applied to early caries: PEARL Network findings.
Bibliographic record
Abstract
The International Caries Detection and Assessment System (ICDAS II) and the Caries Classification System (CCS) are caries stage description systems proposed for adoption into clinical practice. This pilot study investigated clinicians' training in and use of these systems for detection of early caries and recommendations for individual tooth treatment. Patient participants (N = 8) with a range of noncavitated lesions (CCS ranks 2 and 4 and ICDAS II ranks 2-4) identified by a team of calibrated examiners were recruited from the New York University College of Dentistry clinic. Eighteen dentists-8 from the Practitioners Engaged in Applied Research and Learning (PEARL) Network and 10 recruited from the Academy of General Dentistry-were randomly assigned to 1 of 3 groups: 5 dentists used only visual-tactile (VT) examination, 7 were trained in the ICDAS II, and 6 were trained in the CCS. Lesion stage for each tooth was determined by the ICDAS II and CCS groups, and recommended treatment was decided by all groups. Teeth were assessed both with and without radiographs. Caries was detected in 92.7% (95% CI, 88%-96%) of the teeth by dentists with CCS training, 88.8% (95% CI, 84%-92%) of the teeth by those with ICDAS II training, and 62.3% (95% CI, 55%-69%) of teeth by the VT group. Web-based training was acceptable to all dentists in the CCS group (6 of 6) but fewer of the dentists in the ICDAS II group (5 of 7). The modified CCS translated clinically to more accurate caries detection, particularly compared to detection by untrained dentists (VT group). Moreover, the CCS was more accepted than was the ICDAS II, but dentists in both groups were open to the application of these systems. Agreement on caries staging requires additional training prior to a larger validation study.
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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 | 0.016 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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".