Assessment of Fibroblast growth factor-1 as a serum marker in patients with hypertrophic scars undergoing CO2 fractional laser
Bibliographic record
Abstract
BackgroundFractional CO2 laser has shown a great efficacy for the treatment of hypertrophic scars; however, there are different levels of therapeutic outcomes depending on many factors. The aim of this study was to detect any relation between serum level of fibroblast growth factor-1 and the response of the hypertrophic scars to the treatment with fractional CO2 resurfacing.MethodsTwenty patients with hypertrophic scars were included in the study. A serum sample was taken from each patient to assess the serum level of fibroblast growth factor 1. Each patient was subjected to fractional carbon dioxide laser with a frequency of a session every month. Evaluation of the scars was done before starting treatment and after each 3 consecutive sessions by Vancouver score scale (VSS). ResultsAnalysis of the results showed that there is a statistically significant improvement of the hypertrophic scars by using fractional CO2 laser resurfacing, but the percent of improvement showed insignificant correlation with the age, body mass index (BMI) of patients and disease duration. Also, the results revealed that Serum fibroblast growth factor 1 had insignificant correlation with the percent of improvement of the scars treated. ConclusionSerum fibroblast growth factor-1 can’t be used as a marker to predict the probable response of the hypertrophic scars to the treatment with fractional CO2 laser.
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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 | 0.001 |
| 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 | 0.000 |
| 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".