Identifying global trends from case reports of fibrodysplasia ossificans progressiva: a scoping review
Bibliographic record
Abstract
Background: Fibrodysplasia ossificans progressiva (FOP) is an ultra-rare, disabling, genetic disorder characterized by progressive heterotopic ossification (HO). FOP care pathways are complex, and there are many underrepresented patients globally. Objectives: This scoping review aimed to identify global FOP case reports and investigate real-world management practices. Evidence sources eligibility criteria and charting methods: International/local databases and congress proceedings were searched. Publications reporting patient-level data for ⩾1 patient with an FOP primary diagnosis, published January 2000-January 2024, were eligible. Publication details, patient demographics, disease presentation, and care pathway were extracted. Data were charted through categorization of themes and frequency analyses. Results: Of 6064 publications screened, 369 were eligible, reporting on 541 patients with FOP. Most publications (82.4% (304/369)) reported an individual case; the remaining reported 2-25 cases. Cases were from 57 countries, most commonly in the Asia/Pacific region (47.0% (254/541)). Most patients had reportedly seen a single specialist (64.8% (59/91)), and care from multidisciplinary teams was reported for only 5.5% (5/91). HO (84.7% (458/541)) and great toe malformations (66.7% (361/541)) were the most commonly reported signs, though mobility limitations (46.2% (96/208)) and growths/lesions (38.5% (80/208)) were most common before diagnosis. The predominant drug classes reported for FOP treatment were corticosteroids (52.6% (102/194)) and nonsteroidal anti-inflammatory drugs (39.7% (77/194)). Surgeries were reported in 35.1% (190/541) of patients. Where specified, the most common surgery types were malformation correction/bone resection (43.6% (78/179)) and biopsies (34.1% (61/179)). Conclusion: This scoping review synthesized valuable real-world insights from case reports of FOP, potentially supporting outreach initiatives in underserved regions and facilitating shared learnings on diagnosis and treatment. Although the inherent selection bias of case studies toward atypical cases limits definitive conclusions, some global trends in the clinical management practices were revealed.
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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 | 0.016 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.062 | 0.050 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".