Dental care in children with medical complexity: A retrospective study
Bibliographic record
Abstract
Background and Objectives: Children with medical complexity (CMC) are defined by complex, chronic multi-system disease with significant medical fragility. Limited research exists on dental care in CMC, which is an important part of oral health and overall health. Objectives of this study were to (1) determine the frequency and type of dental visits at a tertiary paediatric hospital of all CMC between 2015 and 2020 and (2) identify the factors associated with dental visits. Methods: A retrospective chart review of the electronic records of CMC who were seen at a paediatric hospital from 2015 to 2020 was completed. The number and type of dental visits, demographic and clinical information were reviewed. Poisson regression models were used to test the association between the outcome (number of dental visits) and potential factors associated with receiving dental care. Results: Four hundred and eighty-seven CMC (mean age=7.3 ± 4.6 years, 43.7% female) were included in this study. CMC were seen by dentists at the hospital 4.4 ± 3.8 times since 2015, which is approximately once per year over a 5-year period. Dental visits were mostly preventative (66.4% of all visits). CMC had more dental visits if they had dental care funding compared to no funding if they were living in a community with a population >100,000 people and if they were being followed by a greater number of sub-specialists. Conclusions: This study highlights the importance of funding, access to paediatric dental specialists, and care coordination support to improve access to dental care for CMC to optimize oral health.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".