Association between vitamin D and cholesterol in post-acne hypertrophic scars and keloids: A cross-sectional study
Bibliographic record
Abstract
Background: Post-acne hypertrophic scars and keloids result from abnormal wound healing within the pilosebaceous unit. Ruptured microcomedones create wounds that can lead to prolonged inflammation, increased collagen synthesis, and fibrinolysis inhibition, resulting in elevated scar tissue in acne lesions. Vitamin D deficiency can lead to prolonged inflammation and damage to dermal collagen. High total cholesterol levels can lead to excessive sebum production, exacerbating inflammation and fibrosis. This study aimed to determine the association between serum vitamin D and total cholesterol levels with scar severity in patients with post-acne hypertrophic scars and keloids. Methods: This analytical cross-sectional study was conducted at Dr. Mohammad Hoesin General Hospital, Palembang, South Sumatera, Indonesia, among patients who met the inclusion and exclusion criteria. Scar severity was assessed using the Vancouver Scar Scale (VSS). Blood samples were collected for examination of serum vitamin D and total cholesterol. The data were analyzed statistically. Results: Chi-square test of serum vitamin D with VSS scores showed a significant association (p-value = 0.007), while total cholesterol with VSS showed no association (p-value = 1.000). Odd-ratio (OR) for serum vitamin D and VSS was 60, concluding that vitamin D deficiency increased the risk of high VSS by 60-fold in study patients. Conclusion: There was a significant association between serum vitamin D levels and VSS score, while there was no association between total cholesterol and VSS score. Deficiency of serum vitamin D is associated with a higher risk of developing a higher VSS score in patients with post-acne hypertrophic scars and keloids.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".