Performance-based Competency Standard Setting in Dental Radiograph Interpretation: Balancing Performance Expectations with Patient Care Measures and Learner Experience
Bibliographic record
Abstract
Introduction: Web-based deliberate practice enhances pediatric bitewing radiograph interpretation skills; however, a universally accepted, evidence-based competency standard for dental students has not been established.Objective: Establish a competency standard that balances patient care measures with learner feasibility. Study Design: A single-center controlled trial involving 119 dental students randomly assigned to competency standards of 60% (G1), 70% (G2), or 80% (G3) sensitivity. Outcomes compared across groups included interpretation difficulty, proportion of high-risk (HR) lesions, feasibility (cases completed and time spent), and competency attainment rates. Learner feedback also informed feasibility. Results: Interpretation difficulty scores were significantly lower in higher threshold groups (p<0.001). The proportion of HR lesions among the most difficult cases did not differ between groups (G1=60.0%, G2=58.8%, G3=63.1%; p=0.86). Median cases required to reach competency increased by group (G1=30; G2=49; G3=116, p<0.001), as did time spent (G1=20.4 min; G2=33.1 min; G3=47.5 min, p<0.001). Attainment rates were G1=100%, G2=100%, G3=92.5% (p=0.05). Learner feedback was largely neutral-to-positive, with most preferring a pass score of ≥70%. Conclusion: Higher competency standards decreased interpretation difficulty scores. However, there was no difference in the proportion of HR lesions amongst the most difficult cases between the groups. Given that significantly more effort was required to achieve the higher standards, we conclude that the 70% competency standard best serves both patient care measures and student feasibility.
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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 | 0.017 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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".