Prediction of lower incisor proclination during Xbow treatment based on initial cephalometric variables
Bibliographic record
Abstract
OBJECTIVE: To predict lower incisor proclination from initial cephalometric values in Class II division 1 patients treated in phase I with the Xbow appliance. MATERIALS AND METHODS: Two hundred forty-nine mild to moderate Class II division 1 patients treated with the Xbow appliance as a phase I treatment were considered. Patients were in late mixed dentition or early permanent dentition. Commonly used cephalometric variables at T1 (before treatment) were used to predict lower incisor proclination after Xbow treatment (T2). A principal component analysis (PCA) was performed. The four extracted PCA components were skeletal component, incisal distance, anterior facial projection, and maxillo-mandibular relation. Thereafter, a multiple linear regression analysis (MLRA) was performed using the four extracted PCA components at T1 as predictor variables, and lower incisor inclination relative to the mandibular plane (L1-MP) at T2 as the dependent variable. RESULTS: The mean L1-MP at T1 was 95.46 degrees and the mean L1-MP at T2 was 98.51 degrees, resulting in a mean difference of 3.04 degrees. Only incisal distance and maxillo-mandibular relation PCA components had significance (P < .05) according to the MLRA. The overall model gave an adjusted R2 value (coefficient of determination) of 0.091. CONCLUSION: The best prediction model could account for only 9% of the total variability. Using common cephalometric variables at T1, average lower incisor proclination from Xbow treatment cannot be predicted in a clinically meaningful way.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.002 | 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".