Quantitative assessment of orthodontic treatments performed by graduate orthodontic students of an International School of Dentistry
Bibliographic record
Abstract
CONTEXT: Assessing and monitoring treatment quality within graduate courses are vital for maintaining high standards and addressing areas needing improvement. In this study, we evaluated the treatment quality delivered by measuring the total scores obtained from the comprehensive clinical assessment (CCA) index and the objective grading system (OGS) index. AIMS: This study aimed to evaluate the quality of orthodontic treatments administered by graduate students by using the CCA index and OGS index. SETTINGS AND DESIGN: The research was carried out at the Orthodontic Department of Mashhad School of Dentistry, focusing on a cross-sectional analysis of orthodontic treatments administered by graduate students. METHODS AND MATERIAL: The study randomly selected 36 cases from the department’s archives and evaluated them through pre- and post-treatment analyses, including radiographs, photographs, and dental casts. The evaluation employed the CCA and the OGS indices. STATISTICAL ANALYSIS USED: Data analysis was performed using the Spearman and Mann-Whitney tests. RESULTS: The CCA correlated with the patient’s age and gender and was statistically significant ( P = 0.007 and P = 0.010, respectively). The CCA index was substantially associated with treatment duration ( P = 0.032 a). There was no significant relationship between OGS and CCA ( P = 0.223). The mean of post-treatment OGS was 18.97. All criteria of OGS and the overall score were improved significantly ( P < 0.001). CONCLUSIONS: The results of this study suggested that orthodontic treatment results provided by the investigated program have high scores and meet high standards of the CCA and OGS indices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".