MO059RISK FACTORS FOR SEVERE RENAL DISEASE IN BARDET-BIEDL SYNDROME - PHENOTYPIC AND GENOTYPIC ANALYSES OF THE LARGEST REPORTED AFFECTED COHORT
Bibliographic record
Abstract
Introduction and Aims: The high frequency of renal disease in BBS - a rare autosomal recessive ciliopathy characterised by rod cone dystrophy, renal malformations, learning difficulties, obesity, post-axial polydactyly and hypogonadism - is a cause of great anxiety among patients due to the devastating effect this can have on quality of life, morbidity and mortality. Nineteen disease causing genes have been identified (BBS1-BBS19) in the last two decades coding for proteins that localise to the cilia or the basal body. Sequencing of known disease causing genes confirms a clinical diagnosis of BBS in around 80% of patients. The primary renal phenotype is highly variable ranging from cystic tubular disease, dysplastic renal disease and focal segmental glomerulosclerosis to concentrating defects. Secondary renal disease may occur as a consequence of hypertension and diabetes which are frequently observed in this population. Methods: Three hundred and fifty patients attended the adult and paediatric national BBS clinics in Birmingham and London over a four year period (2010-14). All patients were clinically examined, and genotyped, most were subjected to renal imaging, blood and urine testing.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".