Development of the Evidence-Informed “OI Splint Kit” for Children with Osteogenesis Imperfecta and Their Families
Bibliographic record
Abstract
Background: Children with osteogenesis imperfecta (OI) live in fear of fracturing a bone. As fractures are unpredictable, there is a need for tools and knowledge to immobilize a fracture during emergencies. Inspired by a patient recognized in their local OI community for fracture management, the aim of this patient-initiated project was to establish best practices for the safe handling of fractures, including the creation of an evidence-informed OI Splint Kit. Methods: A systematic review of the literature was conducted to identify kits and tools used to immobilize fractures during emergencies. An expert Task Force consisting of patients, clinicians, and decision makers was conjured to review the synthesized results. Priorities were delineated and a timeline was established to create the OI Splint Kit. The prototype underwent iterative cycles of modifications based on feedback from the Task Force. Results: Four electronic data bases were searched (Medline, CINHAL, PsychInfo, and Scopus), revealing zero publications pertaining to kits for fracture immobilization. The Task Force used their clinical expertise and patient experiences to develop the OI Splint Kit. The following items were included: splinting tools, bilingual educational material, instructional cards, video tutorials, and a memory card game. Conclusion: A gap remains in validated kits to assist in fracture immobilization during emergencies. A tangible and practical OI Splint Kit was developed to fulfill this need, based on inter-professional clinical expertise and patient experiential knowledge. The kit is subject to rigorous testing and ongoing quality evaluations, ensuring it will be suitable for use in various contexts.
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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 | 0.033 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".