Representing Dental Caries and Dysbiosis within the Oral Microbiome in the Oral Health and Disease Ontology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Dental caries is an oral health condition in which cariogenic bacteria demineralize and decay teeth. It arises due to interaction between the host, environment, and oral microbiome. Current terminologies and ontologies, however, do not accurately represent the important role that the microbiome has in the formation of carious lesions. Rather, they focus on the anatomical features of carious lesions and often obfuscate the distinctions between dental caries as a disease affecting a tooth, as lesions that are produced because of the disease, and as lesions produced as a result of dysbiosis in the oral microbiome. To capture the current state of evidence and provide flexibility for evolving literature on host-environment-microbiome interactions, there is a need to revise and expand the ontological framework for dental caries. RESULTS: Several established terminologies and ontologies were reviewed for terms used to represent dental caries and the oral microbiome. We found that they either did not represent or misrepresented the current scientific understanding of caries and its relation to the microbial dysbiosis. As a result of these deficiencies, we added terms and relations to the Oral Health and Disease Ontology (OHD) that more accurately represent how oral microbial dysbiosis influences the development of dental caries. CONCLUSIONS: The Oral Health and Disease Ontology is an advance over existing ontologies for representing the impact of oral microbial dysbiosis on dental caries. It provides a semantic framework that better serves the needs of cariology researchers and can more easily incorporate new oral microbiome findings.
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.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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 it