Diagnosing Developmental Defects of Enamel: Pilot Study of Online Training and Accuracy.
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to assess dentists' ability to correctly identify and classify development defects of enamel (DDE). METHODS: The modified DDE (MDDE) index was used to classify enamel defects into two types: (1) enamel hypoplasia-pitted, grooved, or missing enamel; or (2) enamel opacity-translucency of enamel not caused by dental caries or fluorosis (can be either demarcated or diffuse). A panel of six experts selected and scored 36 images using the MDDE, and the consensus score was used as the gold standard score in the evaluation of survey respondents. A short training table was developed to match training images to descriptors for the MDDE. A survey, including the training table, was then distributed electronically to 2,036 U.S. dentists and expanded function dental assistants from the Indian Health Service and 6,174 members of American Academy of Pediatric Dentistry. The percent of correct responses was evaluated for each image. RESULTS: Survey respondents (348 total) showed great variability in correct responses for each image, ranging from 41 to 97 percent, for each category of the MDDE. CONCLUSIONS: Enhanced training and calibration on the ability of dental providers is needed to identify the different types of development defects of enamel.
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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 | 0.013 | 0.060 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".