210: The Baby Dental Clinic /la Clinique Bébé-Dent – Feasability and Efficacy of a Community Health Centre Based Infant Oral Health Programme
Bibliographic record
Abstract
Early Childhood Caries (ECC) is considered to be the most common pediatric disease. By the age of five, Québec children are reported to have up to 40% more caries than do children in Ontario or the United States. Combined efforts, including early dental visits, caries risk assessment, appropriate therapeutic interventions and preventive counseling of parents are all essential in reducing the risk of ECC. Despite the importance of early dental visits, multiple factors act as barriers to early first oral examinations. The baby dental clinic (BDC) is a novel project that was launched to overcome those barriers and address the oral health needs of inner city infants making early dental visits available in community health centre setting. To assess the feasibility and efficacy of the BDC by reporting the results of the programme for its first two years A retrospective chart review of all BDC users was conducted from September 2011 to December 2013. The feasibility metric included the number of infants referred to the clinic. The efficacy metrics included the number and type of carious lesions detected. A total of 244 infants were seen at the BDC. Most of them were referred by immunization clinics (33%), community healthcare providers (19%) and daycares (15%). A total of 58 infants had carious lesions on their first visit. The average DFMS1–4 rate was of 2.25 at the first visit. The most affected surfaces and teeth were the buccal surfaces of the upper incisors. The BDC was feasible and efficacious while being focused on preventive dental care. More research is needed to measure the long-term impact of early dental visits in this patient population.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".