Genetic influence on scar vascularity after burn injury in individuals of European ancestry: A prospective cohort study
Bibliographic record
Abstract
After burn injury there is considerable variation in scar outcome, partially due to genetic factors. Scar vascularity is one characteristic that varies between individuals, and this study aimed to identify genetic variants contributing to different scar vascularity outcomes. An exome-wide array association study and gene pathway analysis was performed on a prospective cohort of 665 patients of European ancestry treated for burn injury, using their scar vascularity (SV) sub-score, part of the modified Vancouver Scar Scale (mVSS), as an outcome measure. DNA was genotyped using the Infinium HumanCoreExome-24 BeadChip, imputed to the Haplotype Reference Consortium panel. Associations between genetic variants (single nucleotide polymorphisms) and SV were estimated using an additive genetic model adjusting for sex, age, % total body surface area and number of surgical procedures, utilising linear and multinomial logistic regression. No individual genetic variants achieved the cut-off threshold for significance. Gene sets were also analysed using the Functional Mapping and Annotation (FUMA) platform, in which biological processes indirectly related to angiogenesis were significantly represented. This study suggests that SNPs in genes associated with angiogenesis may influence SV, but further studies with larger datasets are essential to validate these findings.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".