Influence of heritability on occlusal traits: a systematic review of studies in twins
Bibliographic record
Abstract
Abstract Background The aim of this systematic review was to identify, evaluate, and provide a current literature about the influence of heritability on the determination of occlusal traits. Materials and methods MEDLINE, SCOPUS, Web of Science, LILACS, and Google Scholar were searched without restrictions up to March 2020. Studies with twin method were considered and the risk of bias assessment was performed using quality of genetic association studies checklist (Q-Genie). The coefficient of heritability (h2), model-fitting approaches, and coefficient correlation were used to estimate the genetic/environmental influence on occlusal traits. The GRADE tool was used to assess the quality of the evidence. Results Ten studies met the eligibility criteria. Three studies presented good quality, five moderate quality, and two poor quality. Most studies have found that the intra-arch traits, mainly the maxillary arch morphology, such as width (h2 16–100%), length (h2 42–100%), and shape (h2 42–90%), and the crowding, mainly for mandibular arch (h2 35–81%), are under potential heritability influence. The traits concerning the inter-arch relationship, as overjet, overbite, posterior crossbite, and sagittal molar relation, seem not to be genetically determined. The certainty of the evidence was graded as low for all outcomes. Conclusions Although weak, the available evidence show that the heritability factors are determinant for the intra-arch traits, namely, arch morphology and crowding. Possibly due they are functionally related, the occlusal traits concerning the maxillary and mandibular relationship seem to have environmental factors as determinants. In this scenario, early preventive approaches can offer a more effective and efficient orthodontic treatment.
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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 | 0.019 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".