Palatal volume and area assessment on digital casts generated from cone-beam computed tomography scans
Bibliographic record
Abstract
OBJECTIVES: The objective of this study was to develop a reproducible method to measure the change of palatal volume and area through superimposition using maxillary expansion digital cast models. MATERIALS AND METHODS: A total of 10 pre- and 10 postexpansion dental cast models were scanned by the same cone-beam computed tomography machine. Superimposition was performed using a fully automated surface-best fit of the palatal surfaces on the digital cast models. A gingival plane, identified only once on superimposed casts, and a distal plane with the lateral closing border and the palatal surface were used to localize this selection of air. Area and volume were calculated for pre- and postexpansion records. Pre- and postexpansion palatal volume and area were measured by the main investigator and three different observers for inter- and intra-observer reproducibility assessment. RESULTS: The level of intra- and inter-observer agreement was very strong (intraclass correlation coefficients ≥ 0.953; P value < .0001) for all measurements. CONCLUSIONS: Palatal volume and area measurements based on the proposed superimposition are reproducible and can be used reliably.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".