Implications of Vertebrate Craniodental Evo‐Devo for Human Oral Health
Bibliographic record
Abstract
Highly processed diets eaten by postindustrial modern human populations coincide with higher frequencies of third molar impaction, malocclusion, and temporomandibular joint disorders that affect millions of people worldwide each year. Current treatments address symptoms, not causes, because the multifactorial etiologies of these three concerns mask which factors incline certain people to malocclusion, impaction, and/or joint issues. Deep scientific curiosity about the origins of jaws and dentitions continues to yield rich insights about the developmental genetic mechanisms that underpin healthy craniodental morphogenesis and integration. Mounting evidence from evolution and development (Evo-Devo) studies suggests that function is another mechanism important to healthy craniodental integration and fit. Starting as early as weaning, softer diets and thus lower bite forces appear to relax or disrupt integration of oral tissues, alter development and growth, and catalyze impaction, malocclusion, and jaw joint disorders. How developing oral tissues respond to bite forces remains poorly understood, but biomechanical feedback seems to alter balances of local bone resorption and deposition at the tooth-bone interface as well as affect tempos and amounts of facial outgrowth. Also, behavioral changes in jaw function and parafunction contribute to degeneration and pain in joint articular cartilages and masticatory muscles. The developmental genetic contribution to craniodental misfits and disorders is undeniable but still unclear; however, at present, human diet and jaw function remain important and much more actionable clinical targets. New Evo-Devo studies are needed to explain how function interfaces with craniodental phenotypic plasticity, variation, and evolvability to yield a spectrum of healthy and mismatched dentitions and jaws.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".