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Record W3113193050 · doi:10.1007/978-3-030-44766-3_15

Japan Scar Workshop (JSW) Scar Scale (JSS) for Assessing Keloids and Hypertrophic Scars

2020· book-chapter· en· W3113193050 on OpenAlexaboutno aff
Rei Ogawa

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldMedicine
TopicDermatologic Treatments and Research
Canadian institutionsnot available
Fundersnot available
KeywordsHypertrophic scarKeloidHypertrophic scarsMedicineScarsGrading scaleScar tissueGrading (engineering)SurgeryDermatology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.858
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.057
GPT teacher head0.326
Teacher spread0.269 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations13
Published2020
Admission routes1
Has abstractyes

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