Current status of predoctoral implant dentistry education – student’s didactic performance and self-assessment: A Systematic Review.
Bibliographic record
Abstract
Abstract Objectives: To describe the current state of predoctoral dental implant education in terms of educational outcomes and the student’s perception of the associated curriculum. Methods: A database search was conducted using Medline (OVID), EMBASE, ERIC (Education Resources and Information Centre) and Web of Science electronic sources. Two reviewers thoroughly reviewed the papers in accordance with the specific selection criteria after carefully choosing the abstracts that seemed to meet the initial selection criterion for full article retrieval. Results: 15 articles were included, which were divided into two distinct groups: those that addressed educational outcomes and those that addressed students’ perceptions. Knowledge was assessed by questionnaire surveys, and it was found that most of the students were poorly to moderately well informed. There was a positive increase in student perception after taking the implant courses. Clinical significance: Although predoctoral education in most dental schools across the world now includes implant dentistry as a core component, the degree of integration varies greatly. To increase the proficiency of predoctoral students around the world in performing implant treatments, it is necessary, according to this systematic review, to create a uniform, well-structured predoctoral implant curriculum and guidelines that include didactic, laboratory, preclinical, and clinical components.
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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 | 0.018 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".