A Cleft-Customized Occlusal Rating System to Assess Orthodontic Occlusal Improvement in Patients With Unilateral Cleft Lip and Palate
Bibliographic record
Abstract
OBJECTIVE: This study aimed to develop a new method to quantify occlusal improvement in patients with unilateral cleft lip and palate (UCLP) who had undergone orthodontic treatment and to evaluate its reproducibility. DESIGN: A panel of orthodontists decided on the relevance of different occlusal features to score initial and final 3-dimensional study models and panoramic radiographs. A subsequent subjective analysis was later performed by a local orthodontic panel. SETTING: The sample was obtained from the orthodontic clinical archives of a hospital known for the treatment of patients with craniofacial differences. PATIENTS: Thirty-one nonsyndromic patients, 17 males and 14 females, were randomly selected according to preestablished inclusion/exclusion criteria. INTERVENTIONS: The records corresponded to the period during which the patients were treated with conventional multibracket mechanics and adjunctive restorative procedures. MAIN OUTCOME/MEASURES: The intraclass correlation coefficient measured intraexaminer and interexaminer agreements. The Spearman correlation test assessed the relationship between the local orthodontic panel perception and the improvement scores. RESULTS: Inter- and intra-rater ICCs varied between fair/good to excellent. There was a strong correlation between the Cleft-Customized Occlusal Rating system classification of occlusal improvement and the local orthodontic panel's perception, thereby enabling the utilization of the interpretation scale by the panel. CONCLUSIONS: The method showed to be a useful tool in quantifying and classifying occlusal improvement in this specific population. As any other method, some limitations apply and need to be accounted for.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".