Molar incisor hypomineralisation: Teaching and assessment across the undergraduate dental curricula in the UK
Bibliographic record
Abstract
BACKGROUND: No consensus exists on how molar incisor hypomineralisation (MIH) should be covered by the undergraduate dental curricula. AIM: To assess the current teaching and assessment of MIH in the UK. DESIGN: A piloted questionnaire regarding the teaching and assessment of MIH was disseminated to paediatric, restorative and orthodontic teaching leads in each UK dental school (n = 16). Data were analysed using descriptive statistics, chi-squared and Kruskal-Wallis tests. RESULTS: Response rates from paediatric, restorative and orthodontic teams were 75% (n = 12), 44% (n = 7) and 54% (n = 8), respectively. Prevention of caries, preformed metal crowns, anterior resin composites and vital bleaching were taught significantly more by paediatric teams (p = .006). Quality of life and resin infiltration were absent from restorative teaching. Orthodontic teaching focussed on the timing of first permanent molar extractions. Paediatric teams were mainly responsible for assessment. Risk factors, differential diagnoses for MIH and defining clinical features were more likely to be assessed by paediatric teams than by others (p = .006). All specialities reported that students were prepared to manage MIH. CONCLUSION: Molar incisor hypomineralisation is primarily taught and assessed by paediatric teams. No evidence of multidisciplinary or transitional teaching/assessment existed between specialities. Developing robust guidance regarding MIH learning in the UK undergraduate curricula may help improve consistency.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".