Preface to the 9th Biennial COAST Conference: Harnessing Technology and Biomedicine for Personalized Orthodontics
Bibliographic record
Abstract
OBJECTIVE: The Consortium on Orthodontic Advances in Science and Technology (COAST) convened for its 9th biennial conference titled 'Harnessing Technology and Biomedicine for Personalized Orthodontics' to explore cutting-edge craniofacial research towards building the foundations for precision care in orthodontics. SETTING AND SAMPLE POPULATION: Seventy-five faculty, scholars, private practitioners, industry, residents and students met at the UCLA Arrowhead Lodge on 6-9 November 2022 for networking, scientific presentations and facilitated discussions. Thirty-three speakers provided state-of-the-art, evidence-based scientific and perspective updates in craniofacial and orthodontic-related fields. The overall format included an Education Innovation Award Faculty Development Career Enrichment (FaCE) workshop focused on faculty career development, three lunch and learns, keynote or short talks and poster presentations. MATERIAL AND METHODS: The 2022 COAST Conference was organized thematically to include (a) genes, cells and environment in craniofacial development and abnormalities; (b) precision modulation of tooth movement, retention and facial growth; (c) applications of artificial intelligence in craniofacial health; (d) precision approaches to Sleep Medicine, OSA and TMJ therapies; and (e) precision technologies and appliances. RESULTS: The collective advances in orthodontics and science represented in the manuscripts of this issue fulfil our goal of laying solid foundations for personalized orthodontics. Participants elevated the need for stronger industry-academic research partnerships to leverage knowledge gained from large datasets with treatment approaches and outcomes; systematizing the potential of big data including through multi-omics and artificial intelligence approaches; refining the genotype: phenotype correlation to create biotechnology that will rescue inherited dental and craniofacial defects; evolving studies of tooth movement, sleep apnoea and TMD treatment to accurately measure dysfunction and treatment successes; and maximizing the integration of newer orthodontic devices and digital workflows. CONCLUSIONS: Technological advances combined with those in biomedicine and machine learning are rapidly changing the delivery of health care including that in orthodontics. These advances promise to lead to enhanced customization, efficiencies and outcomes of patient care in routine orthodontic problems and in severe craniofacial problems, OSA and TMD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".