Screening, diagnostic, and monitoring approaches of Bardet-Biedl Syndrome: A scoping review
Bibliographic record
Abstract
Bardet-Biedl Syndrome (BBS) is a rare, autosomal recessive, multisystemic ciliopathy. Providing care for BBS presents challenges due to limited data. This scoping review aimed to characterize evidence for screening, diagnosing, and monitoring BBS. We searched ten databases for citations published in English and Spanish between January 2017 and October 2023. We selected human-based research that utilized methods to assess BBS, including experimental, quasi-experimental, observational studies, reviews, and guidelines. Screening and data extraction were performed by two independent reviewers, with a third reviewer involved to resolve disagreements. We employed descriptive statistical analyses and qualitative synthesis. We included 113 articles from 32 countries, mainly constituting case reports (n=45, 39.8%). Prenatal ultrasound was the most frequently reported screening method (n=15, 13.3%) for detecting early BBS indicators. Clinical manifestations were crucial in raising suspicion of BBS, with nearly all references adopting the diagnostic criteria by Forsythe and Beales. Central obesity (n=80, 70.8%), postaxial polydactyly (n=73, 64.6%), and retinal rod-cone dystrophy (n=56, 49.5%) were the most frequently documented manifestations. Genetic testing was also essential to diagnosing BBS, with techniques such as next-generation sequencing confirming up to 80% of cases. Articles reported variants in a total of 41 genes, including those encoding BBSome proteins, chaperones, and components of the IFT. Furthermore, we identified the most frequently assessed clinical features during patient follow-up. Notably, we observed that few articles reported complementary exams to evaluate BBS's clinical manifestations. Our results provide valuable insights for healthcare professionals, facilitating evidence-based, ongoing care for patients with BBS.
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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 | 0.015 | 0.067 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.025 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| 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".