A Best Practice Initiative to Optimize Transfer of Young Adults With Osteogenesis Imperfecta From Child to Adult Healthcare Services
Bibliographic record
Abstract
PURPOSE: The aims of this study were (1) to review the current body of knowledge on the transition experiences of adolescents with osteogenesis imperfecta (OI) and appraise the evidence available on transfer summary tools, (2) to develop guidelines for the successful transition of adolescents with OI from child to adult healthcare services, and (3) to create a transfer tool tailored to adolescents with OI. DESIGN AND METHODS: This knowledge synthesis study was overseen by an interprofessional expert task force at Shriners Hospitals for Children-Canada and entailed (1) review of the literature, (2) development of guidelines, and (3) creation of a tool. RESULTS: The tool was created from evidence compiled from case reports, clinical examples, and nonexperimental studies. CONCLUSION: The transfer tool proposes guidelines designed to facilitate a smooth transition from child to adult healthcare services. It also offers creation of a clinically meaningful, person-focused, OI transfer tool that may in turn help improve the transition experience for adolescents. This study significantly contributes to the dearth of literature on transition experiences in OI and on transfer tools in general. Future research is needed to evaluate the implementation and evaluation of the OI transfer tool in practice.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".