The utility of the Toronto system of dental diagnostic codes (TSDDC) to determine prevalence of conditions and to document changes in diagnoses
Bibliographic record
Abstract
This study examined electronic dental records of Toronto Public Health (TPH) to assess utility of the TSDDC to document morbidity, comorbidity, and changes in diagnoses. Using SPSS, the database was restructured for study purposes. Frequencies of conditions and comorbidities were obtained, numbers of patients with changed diagnoses and services they received were documented. In the study population (25860), the top three conditions were caries (78%–81%), deposits (30%–40%), and developmental disturbances (14%–23%). Smooth surface dentinal caries was most prevalent (64%–58%). The top three comorbidities with caries were deposits, developmental disturbances, and defective restorations. Between 66%–70% of all individuals had changes in subsequent diagnoses. The study validated that the TSDDC could be used to report the burden of illness and to document changes in diagnoses at all four digits. To enhance utility in program planning and evaluation the TPH database should be reinforced with software to process the coded diagnostic information.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".