Efficiency of molar distalization with the XBow appliance related to second molar eruption stage
Bibliographic record
Abstract
To quantitatively evaluate on lateral cephalograms horizontal, vertical, and angular changes in the position of the maxillary first molar based on the presence and absence of erupted maxillary second molars when it is distalized with the XBow appliance. In this retrospective study, a total of 102 consecutively treated cases were assessed. Lateral cephalograms were obtained at the start and after completion of active treatment with the XBow appliance. In one group of patients, distal movement of the maxillary first molars was performed before the eruption of maxillary second molars; in the other group of patients, both first and second maxillary molars were simultaneously moved distally. All cephalograms were superimposed on palatal plane using the method of best-fit. In order to compare the mean horizontal, vertical, and angular changes in molar position between the treatment groups and gender, a multivariate analysis of covariance (MANCOVA) was performed with the pre-treatment class II severity used as a covariate. Regression analysis was also performed to further explore any possible relationships between the predictor variables and the quantity and quality of distalization. A MANCOVA revealed that the eruption stage of the maxillary second molar did not have a significant effect on the change in position of the maxillary first molar after treatment with a XBow appliance. When distalizing maxillary first molars with a XBow appliance, there is no difference in the amount of distalization in patients with erupted and unerupted maxillary second molars.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".