Temporal Trends in New Registrations for the Ontario Cleft Lip and Palate/Craniofacial Dental Program Between 2007 and 2018
Bibliographic record
Abstract
BACKGROUND: The Ontario Cleft Lip and Palate/Craniofacial Dental Program was established to fund dental care for those with oral clefts, craniofacial anomalies, congenital oral defects, and acquired facial/oral defects. AIM: To determine the annual rates of newly registered oral clefts, craniofacial anomalies, congenital oral defects, and acquired oral defects cases on the Program from 2007 to 2018 and identify trends in registration rates during this time period. DESIGN: Data were obtained from the Program from 2007 to 2018. Annual, age-specific incidence rates (for age groups 0-4, 5-9, 10-14, 15-19, and 20-24 years) were calculated for new registrations in each diagnostic category, plotted and examined using Poisson regression analysis to identify trends. RESULTS: Rates of new registration for oral clefts and acquired oral defects were stable from 2007 to 2018. The rates of newly registered cases of craniofacial anomalies and congenital oral defects showed an increased trend from 2007 to 2018, although year-by-year comparison of the change in rates did not reach statistical significance. For craniofacial anomalies, congenital oral defects, and acquired oral defects, the highest rates of new registrations occurred in the age group 10 to 14 years, while those aged 0 to 4 recorded the highest registration rates in the oral cleft group. CONCLUSION: Rates in new cleft and acquired oral defects registrations were stable from 2007 to 2018. Rates in newly registered cases of craniofacial anomalies and congenital oral defects increased over the 11-year period.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".