Improving radiographic diagnosis of pulpo‐periodontal complications in primary molars by training: Application in education and clinical research
Bibliographic record
Abstract
INTRODUCTION: The objective of this study was to assess an original learning intervention to train students and paediatric dentistry teachers in radiographic diagnostic accuracy of pulpo-periodontal complications in primary molars. MATERIALS AND METHODS: The learning intervention was based on 250 different randomly ordered radiographs of primary molars within three quizzes (A, B and C) for 5 sessions (S): quiz A (50 X-rays), B and C (100 X-rays) were, respectively, completed in S1 to assess the extent of agreement with 5 experts' diagnoses, in S2 and S3 (B at days 8 and 23) and in S4 and S5 (C at days 90 and 105). During S1 and at the end of S3 and S5, the participants (48 students and 16 teachers) were informed of correct diagnoses. A satisfaction questionnaire was completed by all the students. Alongside the descriptive analyses, generalised linear mixed model (GLMM) analyses assessed the odds of participants' correct diagnosis over the study duration. RESULTS: At S1, the odds of diagnostic accuracy among students were significantly lower than those among the teachers. After receiving feedback at S1, GLMM analyses showed that among all the participants, accuracy improved over time with the odds of correct diagnoses higher in S2-5 than in S1; and there were similar increases across sessions between teachers and students, except in S3, where the improvement among teachers tended to be greater than that among the students. All students were satisfied though one-third reported that quizzes with 100 radiographs felt too long. CONCLUSION: The online case-based learning was a good training format for dental education.
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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 | 0.003 | 0.004 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".