Multiple Supplemental and Impacted Teeth (Polydent): A Non Syndromic Case Report
Bibliographic record
Abstract
Many cases of supernumerary teeth are found in congenital syndromes.Most prevalent dental modifications found, are the number of teeth in genetic conditions affecting ectodermal structures including Cleidocranial Dysostosis (CCD), hypohydrotic ectodermal dysplasia, focal dermal hypoplasia, craniofacial dysostosis, and aperts syndrome.The patient's main complaint was of toothache.Clinically there were no macroscopically detectable abnormalities, and he was unaware of any dental problems.His physical and facial appearance showed no deviations from normal, and he chewed, swallowed and spoke without impediment.A complement of permanent human teeth is usually 32, within four quadrants of the mouth; from the center point there are two incisors, one canine, two pre-molars and three molars.Total 4 × 8 =32 teeth and humans rarely develop more.Presented here is a case with multiple extra supplemental teeth eight in number in his permanent dentition, most of which six were identified radiographically and impacted.This case presented no other identifiable abnormalities in the mouth.The patient's pain derived from one supplemental upper premolar, which was removed.Exodontia other teeth not in function was advised, the patient acknowledged this but failed to return.This unique presentation with supplemental teeth and impactions was deemed to be non-syndromic.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".