Functional genotype classification groups distinguish disease severity in recessive dystrophic epidermolysis bullosa
Bibliographic record
Abstract
BACKGROUND: Recessive dystrophic epidermolysis bullosa (RDEB) is a genetic disorder caused by pathogenic variants in COL7A1. OBJECTIVES: To determine the association between different COL7A1 variants and clinical disease severity in 236 North American patients with RDEB. METHODS: Published reports or in silico predictions were used to assess the impact of pathogenic variants in COL7A1 on type VII collagen (C7) protein function. Three impact categories were postulated: genotypes that would be likely to cause a low impact on C7 function (splice B/missense, missense/missense); a medium impact [premature termination codon (PTC)/splice B, splice A/splice B, PTC/missense, splice A/missense, splice B/splice B]; and a high impact (PTC/PTC, PTC/splice A, splice A/splice A). Splice A variants are predicted to cause downstream PTCs, while splice B variants cause in-frame exon skipping and are therefore less deleterious. RESULTS: The severity of functional impact was significantly associated with a history of gastrostomy tube placement, oesophageal dilation, hand surgery, anaemia, renal disease, chronic wounds, diffuse skin involvement and a history of squamous cell carcinoma. The odds of death were 3.5 time higher in the high-impact vs. medium-impact group (95% confidence interval 1.24-8.50; P = 0.02). Patients in the high-impact group had worse clinical outcomes. CONCLUSIONS: Functional genotype categories are a feasible approach to risk-stratify patients based on predicted C7 function.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".