Should Live Patient Licensing Examinations in Dentistry Be Discontinued? Two Viewpoints
Bibliographic record
Abstract
This Point/Counterpoint article addresses a long-standing but still-unresolved debate on the advantages and disadvantages of using live patients in dental licensure exams. Two contrasting viewpoints are presented. Viewpoint 1 supports the traditional use of live patients, arguing that other assessment models have not yet been demonstrated to be viable alternatives to the actual treatment of patients in the clinical licensure process. This viewpoint also contends that the use of live patients and inherent variances in live patient treatment represent the realities of daily private practice. Viewpoint 2 argues that the use of live patients in licensure exams needs to be discontinued considering those exams' ethical dilemmas of exposing patients to potential harm, as well as their lack of reliability and validity and limited scope. According to this viewpoint, the current presence of viable alternatives means that the risk of harm inherent in live patient exams can finally be eliminated and those exams replaced with other means to confirm that candidates are qualified for licensure to practice.
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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 | 0.047 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.028 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.047 | 0.040 |
| Insufficient payload (model declined to judge) | 0.003 | 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".