Japan Scar Workshop (JSW) Scar Scale (JSS) for Assessing Keloids and Hypertrophic Scars
Bibliographic record
Abstract
Abstract The Vancouver scar scale, the Manchester scar scale, and the Patient and Observer Scar Assessment Scale (POSAS) are all very well-known scar evaluation methods. These tools are based on a number of scar variables, including color, height, and pliability. However, since all were mainly developed to evaluate burn scars, they are difficult to use in clinical practice for keloids and hypertrophic scars. This is because these pathological scars require both differential diagnosis and a way to evaluate their response to therapy. The Japan Scar Workshop (JSW) has sought to develop a scar assessment scale that meets these clinical needs. The first version of this scar assessment tool was named the JSW scar scale (JSS), and it was reported in 2011. In 2015, the revised second version was reported. The JSS consists of two tables. One is a scar classification table that is used to determine whether the scar is a normal mature scar, a hypertrophic scar, or a keloid. This grading system helps the user to select the most appropriate treatment method for the scar. The other table in the JSS is an evaluation table that is used to judge the response to treatment and for follow-up. Both tables contain sample images of each subjective keloid/hypertrophic scar item that allow the user to evaluate each item without hesitation.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.011 |
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".