Dental abnormalities in individuals with pathogenic germline variation in <i>DICER1</i>
Bibliographic record
Abstract
Pathogenic germline variation in the microRNA processing gene DICER1 gives rise to an autosomal dominant, tumor-predisposition disorder. Conditional deletion of Dicer1 in murine dental epithelium shows that it controls tooth patterning, size, number, and shape. The human dental phenotype of people with germline pathogenic variation in DICER1 is unknown. DICER1-carriers (n = 57) and family controls (n = 55) were evaluated at the NIH Clinical Center dental clinic as part of a comprehensive medical evaluation. Digital panoramic radiographs, bite-wing radiographs, and oral photographs were collected. A single observer, blind to DICER1 status, reviewed the dental records and determined the presence or absence of 11 dental characteristics as described in the clinic notes, radiographs, or oral photographs. Subjective phenotypes were reviewed on radiographs by two examiners (blind to DICER1 status) for the presence or absence of the dental characteristics to reduce inconsistencies. By simple association, bulbous crown, periodontitis, and taurodontism were all significant (p < .05). Logistic regression with chi-square maximum likelihood estimates showed that bulbous crown and periodontitis remained significant. Recognition of these phenotypes may aid identification of individuals and families at risk for DICER1-associated neoplasms. These findings may also guide dental care for individuals with germline DICER1 pathogenic variation.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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".