Mandibular Third Molars and Lower Anterior Crowding: Comparison of Opinions of Oral-Maxillofacial Surgeons and Orthodontists
Bibliographic record
Abstract
Objective: To compare the opinion of orthodontists and oral-maxillofacial surgeons on relation between erupting mandibular third molars and lower incisal crowding.Patients and Methods: This descriptive study involved 100 Pakistani clinicians (50 orthodontists, 50 oral-maxillofacial surgeons) to answer online questionnaire regarding their opinions on link between erupting Mandibular Third Molars along with their extraction opinion with reference to development and prevention of lower incisal crowding. Data was analyzed using SPSS version 21.0. Pearson's chi-square test was applied and statistical significance was defined at <=0.05.Results: Statistically insignificant differences were found between oral-maxillofacial surgeons and orthodontists regarding question of erupting mandibular third molars in causing lower incisal crowding. Similarly, statistically insignificant differences between oral-maxillofacial surgeons and orthodontists were found regarding question of recommending preventive extraction of mandibular third molars for developing lower incisal crowding.Conclusion: No opinion differences were observed between Pakistani oral surgeons and orthodontists, regarding the link of lower third molar as a cause of lower incisal crowding.
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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 | 0.001 | 0.006 |
| 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) | 0.004 | 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".