Comparison of Lower Incisor Gingival Recession in Nonextraction Orthodontic Patients with Class I Crowding and Class II Malocclusion
Bibliographic record
Abstract
Objective: To compare lower incisor gingival recession (GR) in non extraction orthodontic patients with Class I crowding and Class II malocclusion treated using Class II elastics. Study Design: A cross sectional comparative study. Place and Duration of Study: Orthodontic Department, Rawal Institute of Health Sciences (RIHS), from February 10, 2024 to August 10, 2024. Materials and Methods: Pre and post-treatment casts of 42 orthodontic patients were divided into two groups: Class I crowding (C1) and Class II elastic treatment (E2). Clinical crown height (CCH) of the lower left central incisor was measured. GR was determined as the difference in CCH before and after treatment. The data was analyzed by SPSS v.20.0. Descriptive statistics like frequency of gender and mean age in C1 and E2 group were calculated. Paired sample t-test for intra group GR (pre and post treatment) and independent sample t-test for inter group GR were applied to analyze GR between two groups. The p value ≤ 0.05 was considered statistically significant. Results: Both groups showed an increase in GR after treatment. The mean GR1 value was slightly higher (.5214mm) than GR2 (.4262mm) depicting that the C1 group had slightly more GR than the E2 group, though this difference was not statistically significant. Conclusion: Both treatment modalities in non extraction cases resulted in increased GR, emphasizing the need to consider periodontal implications during orthodontic planning.
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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 | 0.000 |
| 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".