Prevalence of Distal Cervical Caries in Mandibular Second Molar caused by impacted third molar
Bibliographic record
Abstract
Aim: To analyse early recognition of the distal cervical caries of mandibular second molar caused by impacted mandibular third molar, to correlate oral health and caries status and to find out the average age groups and gender affected by impacted third molar Methods: A cross-sectional survey of 300 participants was conducted over a 15-month period at Rehmat Memorial Hospital, Abbottabad. 300 participants having impacted third molar having distal cervical caries in mandibular second molar were analyzed clinically and radiographically. Data was analyzed using SPSS version 23.0 Results: the demographic data of 300 patients with impacted mandibular third molars were analyzed. 64% were male and 36% female that have extractions due to impaction. Caries caused in more than half of participants by mesioangular impaction, which was 52%, 3% due to distoangular, 26% due to distal, and 18% due to horizontal impaction. In 63.25% of cases, teeth were lost due to caries, periodontitis caused 20.25% of tooth loss, pericoronitis 7.75%, orthodontics 3.75%, prosthodontics 1.2%, trauma 1%, and other factors were 2.5%. study reveals that 30.5 % of the extractions were done from 21 and 30 years and 23 %of extractions were performed. 40% of those who took part in the study did not brush their teeth. Socioeconomic status also has a great impact on tooth extractions. Conclusion: After conducting this study, it was concluded that there was a relationship between the prevalence of distal cervical caries in mandibular second molars and the placement of neighbouring impacted mandibular third molars. As a result, the extraction of mandibular third molars should be done to avoid cavities and premature tooth loss in the neighboring molar. Key words: Third molar impaction, distoangular, distal cervical caries, extraction, 2nd molar caries
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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".