An Educational Method for Learning and Assessing Interproximal Dental Caries Diagnosis in Bitewing Radiographs of Children
Bibliographic record
Abstract
Background: Intraoral bitewing radiographs are the gold standard for diagnosing interproximal caries. There are currently no validated methods for assessing this competency in Canadian dental trainees. Objective: A computerized learning tool was developed to measure and improve performance in caries diagnosis from pediatric bitewing radiograph interpretation amongst dental trainees. Methods: In this multi-centre prospective cohort study, diagnostic performance was measured using learning curves and linear regression analysis identified image variables associated with increased interpretation difficulty of bitewing radiographs. Results: 62 participants completed the case set and a significant increase from mean initial to maximal diagnostic performance was observed (p<0.001). Two image-specific variables were associated with a significantly greater image interpretation difficulty score (p<0.001). Conclusion: The computerized learning platform led to effective and feasible skill improvement in diagnostic performance amongst dental trainees. Significance: The platform may be a valuable adjunct to existing educational methods in dentalradiology for dental trainees.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".