Frequency of mandibular third molar surgical impactions in orthodontic patients with different antegonial notching
Bibliographic record
Abstract
Background: Few studies have been reported so far on the frequency of lower third molar impactions in patients with different morphological characteristics. Our aim in this study was to find out the frequency of impacted lower third molars in orthodontic patients with different antegonial notch depths. Material and Methods: This cross-sectional research was conducted at Orthodontics Department of Dental Section, Faisalabad Medical University, Faisalabad, and de’Montmorency College of Dentistry, Lahore, Pakistan from June 2017 to June 2019. A total of 60 orthopantomograms (OPGs) of patients with impacted lower third molars were included. The depth of antegonial notch was calculated on all the selected OPGs by measuring distance between the deepest area of the notch cavity and the tangent on the inferior border of the mandible. The patients with depth of antegonial notch of 1 mm or less were labeled as having shallow antegonial notch, while those with 3 mm or more were considered as having deep antegonial notch. Percentages and mean + SD were calculated for different variables. Depth of antegonial notching was compared between genders using ANOVA with P-value <0.5 considered as significant. Results: Of 60 orthodontic patients, there was an equal number of male and female patients (n=30). The average age of the patients was 25.5±4 years. Overall frequency of impactions was similar in both the genders and frequency of impacted lower third molars was found to be greater in patients with deeper antegonial notches. Conclusion: Mandibular third molar impactions were most frequent in orthodontic patients with deep antegonial notches
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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) | 0.004 | 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".