Development of a Checklist to Prevent Reconstructive Errors Made By Undergraduate Dental Students
Bibliographic record
Abstract
PURPOSE: To design a checklist in order to reduce the frequency of reconstructive preventable errors (PE) performed by undergraduate dental students at McGill University. MATERIALS AND METHODS: The most common PE occurring at a university dental clinic were identified by three reviewers analyzing the refunded cases, and used to create a preliminary checklist. This checklist was then validated by a panel of dental educators to produce a finalized 20-item checklist. The 20-question checklist was then submitted to students in a cross-sectional survey-based study to evaluate its relevance to undergraduate clinical education needs. RESULTS: As many as 81% of students reported to have forgotten at least one item of the checklist during care of their last patient, and the most forgotten checklist items corresponded to the pretreatment stage. The students also reported that 17 of the 20 items in the checklist were relevant to a considerable extent or highly relevant. CONCLUSION: Common PE identified in the undergraduate clinic could be used to create a checklist of relevant items designed to reduce errors made by students and practitioners performing prosthodontic and reconstructive treatments. However, further studies are required to evaluate the implementation and efficiency of the checklist.
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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 | 0.079 | 0.221 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".