Validation of Fespixon in Postoperative Scar Cosmesis Using Quantitative Digital Photography Analysis
Bibliographic record
Abstract
BACKGROUND: Unsightly scarring after surgery remains a dilemma. One of the challenges is the lack of objective scar assessment tools. OBJECTIVES: This study aimed to evaluate the efficacy of a novel medicine, Fespixon, for prevention and/or alleviation of post-skin incision scarring. A second aim was to demonstrate the practicality of our digital image analysis system to see if this could serve as a sensitive tool to assess scar improvement. METHODS: A prospective, placebo-controlled trial involving patients with postoperative transverse scars was conducted. Each patient received a topical formulation of Fespixon on the left part of the scar and placebo cream on the right. In addition to recording the subjective modified Vancouver Scar Scale and visual analog scale scores, we utilized digital photography for monthly scar analysis, with CIELAB and hue serving as the colorimetric information, and with contrast, correlation, homogeneity, and entropy providing texture information. RESULTS: Forty-six participants (mean age, 52 years) were enrolled in the trial. All the parameters of subjective assessment showed superior results for the Fespixon-treated side, with significant differences in pigmentation, vascularity, pliability, height, itchiness, and patient satisfaction (P = .043, .013, .026, .002, .039, .012, respectively). The trends in color and texture showed increased relative difference ratios, with significant differences in most of the eigenvalues towards the Fespixon-treated side, including CIELAB-L* (P = .000), hue-R,G,B (red, blue, green) values (P = .034, .001, .011), contrast (P = .000), homogeneity (P = .000), correlation (P = .011), and entropy (P = .000). CONCLUSIONS: We validated the efficacy of Fespixon for postoperative scar healing based not only on subjective assessments but also on objective quantitative analyses. The results also indicated that our digital photography quantitative analysis system is an ideal tool for quantification of scar appearance.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.000 | 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".