Genetic Risk Factors for Hypertrophic Scar Development
Bibliographic record
Abstract
Hypertrophic scars (HTSs) occur in 30 to 72% patients after thermal injury. Risk factors include skin color, female sex, young age, burn site, and burn severity. Recent correlations between genetic variations and clinical conditions suggest that single-nucleotide polymorphisms (SNPs) may be associated with HTS formation. The authors hypothesized that an SNP in the p27 gene (rs36228499) previously associated with decreased restenosis after coronary stenting would be associated with lower Vancouver Scar Scale (VSS) measurements and decreased itching. Patient and injury characteristics were collected from adults with thermal burns. VSS scores were calculated at 4 to 9 months after injury. Genotyping was performed using real-time polymerase chain reaction. Logistic regression was used to determine risk factors for HTS as measured by a VSS score >7. Three hundred subjects had a median age of 39 years (range, 18-91); 69% were male and median burn size was 7% TBSA (range, 0.25-80). Consistent with literature, the p27 variant SNP had an allele frequency of 40%, but was not associated with reduced HTS formation or lower itch scores in any genetic model. HTS formation was associated with American Indian/Alaskan Native race (odds ratio [OR], 12.2; P = .02), facial burns (OR, 9.4; P = .04), and burn size ≥20% TBSA (OR, 1.99; P = .03). Although the p27 SNP may protect against vascular fibroproliferation, the effect cannot be generalized to cutaneous scars. This study suggests that American Indian/Alaskan Native race, facial burns, and higher %TBSA are independent risk factors for HTS. The American Indian/Alaskan Native association suggests that there are potentially yet-to-be-identified genetic variants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".