Lower incisor inclination changes during Xbow treatment according to vertical facial type
Bibliographic record
Abstract
OBJECTIVE: To evaluate the magnitude of lower incisor inclination associated with the vertical facial type in adolescent Class II patients treated with the Xbow appliance. MATERIALS AND METHODS: A total of 172 consecutive Class II patients treated with only the Xbow appliance were used. The sample was divided into three groups based on their vertical facial type (24 short, 122 normal, and 25 long facial types). The mean age was 11.11 years at T1 with a mean active Xbow time of 4.5 months. A mean of 6.4 months passed after the Xbow deactivation before T2 radiograph. RESULTS: No significant association between lower incisor proclination and vertical facial type was found. Actual differences between T1 and T2 did exist. In most cases, these differences may be considered clinically relevant, but when the large interindividual variability is considered, the differences between the groups could not be statistically supported. At T1, a distinct trend to have more proclined lower incisors in the short (100.5 degrees ) compared with the long (91.3 degrees ) facial types was found. During treatment, a trend was identified for more proclination of the lower incisor the shorter the face. CONCLUSIONS: Although lower incisors do procline with the use of the Xbow appliance, facial type does not appear to affect the amount of lower incisor inclination. The magnitude of the incisor proclination can be considered not clinically relevant, but a large individual variation in the incisor response was identified.
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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) | 0.000 | 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 | 0.000 |
| 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".