The association between hypodontia and dental development
Bibliographic record
Abstract
OBJECTIVES: In this cross-sectional study, we aimed to investigate the pattern of hypodontia in the Dutch population and determine the association between hypodontia and dental development in children with and without hypodontia, applying three different standards, Dutch, French Canadian, and Belgian, to estimate dental age. METHODS: We used dental panoramic radiographs (DPRs) of 1488 children (773 boys and 715 girls), with a mean age of 9.76 years (SD = 0.24) participating in a population-based cohort study in Rotterdam, the Netherlands, born in 2002-2004, and 452 children (219 boys and 233 girls) with a mean age of 9.83 years (SD = 1.09) participating in a mixed-longitudinal, interdisciplinary population-based cohort study in Nijmegen, the Netherlands born in 1960-1968. RESULTS: The prevalence of hypodontia in the Generation R Study was 5.6 % (N = 84) and 5.1 % (N = 23) in the Nijmegen Growth Study. Linear regression analysis showed that children with hypodontia had a 0.37 [95 % CI (-0.53,-0.21)] to 0.52 [95 % CI (-0.76,-0.38)] years lower dental age than children without hypodontia. The ordinal regression analysis showed a delay in development of mandibular second premolars [1.68 years; 95 %CI (-1.90,-1.46)], mandibular first premolars [0.57 years; 95 % CI (-0.94,-0.20)], and mandibular second molars [0.47 years; 95 % CI (-0.84,-0.11)]. CONCLUSION: These findings suggest that children with hypodontia have a delayed dental development. CLINICAL RELEVANCE: The delay of dental development in children with hypodontia should be taken into consideration and therefore orthodontists should recognize that a later start of treatment in these patients may be necessary.
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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 | 0.004 |
| 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 | 0.001 | 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".