Registration of serial maxillary models via the weighted rugae superimposition method
Bibliographic record
Abstract
OBJECTIVES: We introduce a weighted method for superimposition of serial digital maxillary models based on the variable stability of rugae points. SETTING AND SAMPLE POPULATION: Plaster maxillary models of 24 randomly selected 12-year-olds as well as their models at the age of 14 were obtained and scanned using a benchtop structured-light 3D scanner. METHODS: The models were registered twice, once via the unweighted and again via the proposed weighted rugae superimposition method based on 12 rugae landmarks. For each superimposition, distances between the corresponding rugae points were measured and compared with reported displacements of rugae points in literature. RESULTS: The unweighted superimposition produced no meaningful differences in terms of total displacements of registration landmarks, whereas the weighted method recognized the medial points of the third ruga as the most stable landmarks. Results of the weighted method also demonstrated statistically significant smaller changes for medial rugae points in almost every dimension compared to the lateral rugae points. These results comply with the growth patterns of maxilla and rugae point displacements reported in similar studies. CONCLUSION: Considering the variable stability of rugae points during growth, the weighted rugae superimposition method results in more promising registrations on serial models. This method prioritizes registration landmarks based on clinical criteria of choice and is suitable for analysis of other structures such as tooth movements.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".