A prospective randomized evaluation of scar assessment measures
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: To determine the efficacy of interventions to improve and monitor skin scarring, a valid assessment instrument must be used. Current tools used for the evaluation of skin scarring employ equal appearing interval (EAI) scales that assume scar dimensions conform to linear models. Some scar features meet these assumptions, whereas others may not be accurately described. This study determined if current methods of scar evaluation validly characterize inherent features of scars, and in doing so, empirically validate if specific scar dimensions were best represented by linear or nonlinear mathematical models. STUDY DESIGN: Prospective, randomized, cross-over trial. METHODS: Twenty-seven observers evaluated 30 scar photos utilizing both EAI and direct magnitude estimation (DME) scaling methods. The method of scaling and the assessed dimensions of vascularity, pigmentation, thickness, pliability, and surface area were randomized. EAI and DME data were evaluated to identify whether each scar dimension conformed to linear or curvilinear mathematical models. RESULTS: Best-fit analysis revealed the dimensions of vascularity and pigmentation to be more accurately described using curvilinear functions, whereas pliability, thickness and surface area were best defined using linear models. CONCLUSIONS: The scar dimension under assessment must be considered when attempting to validly apply an assessment instrument. Several commonly evaluated dimensions of skin scarring are not appropriately characterized using linear EAI scales. Thus, present assessment instruments must be revised to account for this aberration to allow for a valid means of objectively evaluating skin scarring.
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 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 | 0.026 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".