Transition to adult dental care from a pediatric hospital dental home for patients with special health care needs
Bibliographic record
Abstract
AIMS: This study describes patients with complex Special Health Care Needs (SHCN) transitioning from a pediatric hospital clinic dental home to adult care and evaluates effectiveness of transition practices. METHODS AND RESULTS: Demographics, medical/behavioral complexity, and documentation of transition processes were collected for patients graduated from the service in 2018/2019. An invitation to complete a survey assessing transition was sent to patients/guardians ≥ 14 months after the final visit. Seventy-nine patients graduated and 94% required accommodation for SHCN: 47% medical, 42% medical + behavioral, and 5% behavioral only. Of 63 eligible patients/guardians, 29 completed surveys. While 90% of surveyed patients had established some/all adult medical care, only 41% completed a dental visit, and less than 28% established a dental home. Medical/behavioral complexity, payer, and time since graduation did not impact having a visit. CONCLUSIONS: This study found ineffectiveness of departmental protocol for transition to adult dental homes for patients with SHCN. Developing an optimal transition process is complex and will require collaboration of all stakeholders. Introducing transition in early teen years, tracking progress at subsequent visits, assessing patient readiness, summarizing history for receiving providers, and verifying transition are elements of medical transition programs that should be included in dental transitions.
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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 | 0.001 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".