Diagnostic codes in dentistry--definition, utility and developments to date.
Bibliographic record
Abstract
Diagnostic codes are computer-readable descriptors of patients' conditions contained in computerized patient records. The codes uniquely identify the diagnoses or conditions identified at initial or follow-up examinations that are otherwise written in English or French on the patient chart. Dental diagnostic codes would allow dentists to access information on the types and range of conditions they encounter in their practices, enhance patient communication, track clinical outcomes and monitor best practices. For the profession, system-wide use of the codes could provide information helpful in understanding the oral health of Canadians, demonstrate improvements in oral health, track best practices system-wide, and identify and monitor the progress of high-need groups in Canada. Different systems of diagnostic codes have been implemented by program managers in Germany, the United Kingdom and North America. In Toronto, the former North York Community Dental Services developed and implemented a system that follows the logic used by the Canadian Dental Association for its procedure codes. The American Dental Association is now preparing for the release of SNODENT codes. The addition of diagnostic codes to the service codes already contained in computerized patient records could allow easier analysis of the rich evidence available on the oral health and oral health care of Canadians, thereby enhancing our ability to continuously improve patient care.
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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 | 0.009 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".