Variation of orthodontic treatment decision-making based on dental model type: A systematic review
Bibliographic record
Abstract
OBJECTIVE: To determine in which clinical scenarios digital models are valid as replacements for plaster models during orthodontic treatment decision-making process and treatment planning. MATERIALS AND METHODS: An attempt to identify all pertinent published information was made. Retained articles were those where a decision-making process leading to differential orthodontic treatment plans based on either method were compared. The search was tailored for PubMed and adapted for EMBASE, MEDLINE, the Cochrane Library, LILACS, and Web of Science. A partial grey literature search was conducted through Google Scholar. References lists of the included articles were screened for potential relevant studies. The methodology of selected studies was evaluated using the Quality Assessment Tool for Diagnostic Accuracy Studies (QUADAS). RESULTS: Only two studies were finally selected for the qualitative and quantitative synthesis. QUADAS results scores from selected studies ranged from 61% to 83% of 11 items evaluated. In one, the overall treatment plan regarding orthognathic surgery for Class II malocclusion changed in 13% to 22% of the cases. In the other one, 6% of the orthodontic treatment plans changed. CONCLUSION: Digital models could be used to replace plaster models in Class II malocclusion treatment planning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".