Mobility in Osteogenesis Imperfecta: A Multicenter North American Study
Bibliographic record
Abstract
Background Osteogenesis imperfecta (OI) is a genetic connective tissue disorder characterized by increased bone fragility and recurrent fractures. The phenotypic severity of OI has a significant influence on the ability to walk but little is known about the ambulatory characteristics, strength, or functional abilities in individuals with OI, especially in the more severe forms. To advance clinical research in OI, the Linked Clinical Research Centers, network of clinical centers in North America with significant experience in treating patients with OI, was established in 2009. The purpose of this work was to characterize mobility in OI using standard clinical assessment tools. and determine if any patient characteristics could be used to predict mobility outcomes. Methods Data were collected at five clinical sites and included age, gender, ethnicity, height, weight, use of assistive device, and bisphosphonate use and mobility metrics (age at first walk, Gillette Functional Assessment Questionnaire, Functional Mobility Scale, and distance walked in the 6 minute walk test). Linear mixed models were developed to explore the relationships between subject demographics and mobility metrics. Results The study identified 491 individuals age 3 and older. In general, the results showed minor limitations in the type I group while the more severe types showed more significant limitations in all mobility metrics analyzed. Height and weight were shown to be the most significant predictors of mobility metrics. Relationships with mobility and bisphosphonates varied with OI type and whether oral or IV was used. Conclusion This paper is the most comprehensive report of mobility in individuals with OI to date. These results are vital to understanding the mobility limitations of specific types of OI and beneficial when developing rehabilitation protocols for this population. It is important for physicians, patients, and caregivers to gain insight into severity and classification of the disease and the influence of disease-related characteristics on the prognosis for mobility.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".