Evaluation of a novel palatal suture maturation classification as assessed by cone-beam computed tomography imaging of a pre- and postexpansion treatment cohort
Bibliographic record
Abstract
ABSTRACT Objectives: To test the reliability and usefulness of the midpalatal suture maturation classification and methodology proposed in 2013 by Angelieri et al. for successful prediction of rapid maxillary expansion (RME) treatment results. Materials and Methods: Reliability testing focused on 16 patients aged 9.5–17 years with early mixed to full permanent dentition, representing all proposed palatal maturation stages, from available preexpansion cone-beam computed tomography (CBCT). A retrospective observational longitudinal (cohort) study evaluated 63 preadolescent and adolescent patients aged 11–17 years with full permanent dentition treated with tooth-borne RME appliances who had CBCT records taken at pre- (T 1 ) and postexpansion (T 2 ). CBCT three-dimensional landmarking produced skeletal and dental widths and dental angulations used to evaluate the extent of skeletal and/or dental expansion. A regression model was used to assess the prediction capability of the T1 palatal suture classification of each subject for dental and skeletal changes. Results: There was almost perfect intraexaminer agreement and slight to poor interexaminer agreement, differing from previously reported reliability, affected by necessary operator calibration and the degree of postacquisition image sharpness and clarity. Further exploration of its scientific basis suggested that the proposed classification was ill-founded. Results from the cohort study were also wholly unsupportive of efficacy of the proposed palatal suture maturation classification in predicting the magnitude of portrayed changes. Conclusions: Clinicians should be cautious in applying this classification. Although it has merits, the palatal classification still needs much more research and validation.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".