The patient clinical journey and socioeconomic impact of osteogenesis imperfecta: a systematic scoping review
Bibliographic record
Abstract
BACKGROUND: Osteogenesis imperfecta (OI) is a rare heritable connective tissue disorder primarily characterised by skeletal deformity and fragility, and an array of secondary features. The purpose of this review was to capture and quantify the published evidence relating specifically to the clinical, humanistic, and economic impact of OI on individuals, their families, and wider society. METHODS: A systematic scoping review of 11 databases (MEDLINE, MEDLINE in-progress, EMBASE, CENTRAL, PsycINFO, NHS EED, CEA Registry, PEDE, ScHARRHUd, Orphanet and Google Scholar), supplemented by hand searches of grey literature, was conducted to identify OI literature published 1st January 1995-18th December 2021. Searches were restricted to English language but without geographical limitations. The quality of included records was assessed using the AGREE II checklist and an adapted version of the JBI cross-sectional study checklist. RESULTS: Of the identified 7,850 records, 271 records of 245 unique studies met the inclusion criteria; overall, 168 included records examined clinical aspects of OI, 67 provided humanistic data, 6 reported on the economic impact of OI, and 30 provided data on mixed outcomes. Bone conditions, anthropometric measurements, oral conditions, diagnostic techniques, use of pharmacotherapy, and physical functioning of adults and children with OI were well described. However, few records included current care practice, diagnosis and monitoring, interactions with the healthcare system, or transition of care across life stages. Limited data on wider health concerns beyond bone health, how these concerns may impact health-related quality of life, in particular that of adult men and other family members, were identified. Few records described fatigue in children or adults. Markedly few records provided data on the socioeconomic impact of OI on patients and their caregivers, and associated costs to healthcare systems, and wider society. Most included records had qualitative limitations. CONCLUSION: Despite the rarity of OI, the volume of recently published literature highlights the breadth of interest in the OI field from the research community. However, significant data gaps describing the experience of OI for individuals, their families, and wider society warrant further research to capture and quantify the full impact of OI.
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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 | 0.013 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.014 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".