The Development of a Dental Diagnostic Terminology
Bibliographic record
Abstract
There is no commonly accepted standardized terminology for oral diagnoses. The purpose of this article is to report the development of a standardized dental diagnostic terminology by a work group of dental faculty members. The work group developed guiding principles for decision making and adhered to principles of terminology development. The members used an iterative process to develop a terminology incorporating concepts represented in the Toronto/University of California, San Francisco/Creighton University and International Classification of Diseases (ICD)-9/10 codes and periodontal and endodontic diagnoses. Domain experts were consulted to develop a final list of diagnostic terms. A structure was developed, consisting of thirteen categories, seventy-eight subcategories, and 1,158 diagnostic terms, hierarchically organized and mappable to other terminologies and ontologies. Use of this standardized diagnostic terminology will reinforce the diagnosis-treatment link and will facilitate clinical research, quality assurance, and patient communication. Future work will focus on implementation and approaches to enhance the validity and reliability of diagnostic term utilization.
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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 | 0.031 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.017 | 0.012 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".