Quantitative analysis of a scar's pliability, perfusion and metrology
Bibliographic record
Abstract
The primary effect of scarring is the loss of function in the affected area. Scarring also leads to physical and psychological problems that could be devastating to the patient’s life. Currently, scar assessment is highly subjective and physician dependent. The examination relies on the expertise of the physician to determine the characteristics of the scar by touch and visual examination using the Vancouver scar scale (VSS), which categorizes scars depending on pigmentation, pliability, height and vascularity. In order to establish diagnostic guidelines for scar formation, a quantitative, accurate assessment method needs to be developed. An instrument capable of measuring all categories was developed; three of the aforementioned parameters will be explored. In order to look at pliability, a durometer which measures the amount of resistance a surface exerts to prevent the permanent indentation of the surface is used due to its simplicity and quantitative output. To look at height and vascularity, a profilometry system that collects the location of the scar in three-dimensions and laser speckle imaging (LSI), which shows the dynamic changes in perfusion, respectively, are used. Gelatin phantoms were utilized to measure pliability. Finally, dynamic changes in skin perfusion of volunteers’ forearms undergoing pressure cuff occlusion were measured, along with incisional scars.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".