A Cephalometric Evaluation of Soft Tissue Following Maxillary Incisors Retraction
Bibliographic record
Abstract
Background: Class 2 Division 1 is the most prevalent type of malocclusion affecting about 32% of Pakistani population. With upper maxillary premolar extraction is one of most frequent treatment choice. Aim: To evaluate the effects of these extractions on soft tissue show variable results depending upon the sex, ethnicity, maxillary arch crowding and pretreatment structure of lips. Methods: In this study pretreatment cephalograms of 106 Class 2 div 1 patients were taken whose treatment plan include extraction of maxillary 1st premolar. Then the second and final cephalograms were taken when retraction of incisors was completed. Mean changes in the position of upper and lower lip were measured with respect to Ricketts E-line before and after completion of retraction of maxillary incisors. Results: After the extraction of premolars there is a significant (P value=0.000) reduction in the lip protrusion of -2.033mm±1.148mm and -1.695mm±1.628mm in both upper and lower lip respectively. Conclusion: Extraction of maxillary premolars cause significant reduction of lip prominence and achieve facial esthetic balance. Keywords: Class 2 div 1, lip position, Premolar Extraction
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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 | 0.001 |
| 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) | 0.003 | 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".