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Record W2530748698 · doi:10.5021/ad.2016.28.5.615

Values of a Patient and Observer Scar Assessment Scale to Evaluate the Facial Skin Graft Scar

2016· article· en· W2530748698 on OpenAlexaboutno aff
Jin Kyung Chae, Jeong Hee Kim, Eun Jung Kim, Kun Park

Bibliographic record

VenueAnnals of Dermatology · 2016
Typearticle
Languageen
FieldMedicine
TopicDermatologic Treatments and Research
Canadian institutionsnot available
FundersWonkwang University
KeywordsMedicineIntraclass correlationSurgeryDermatology

Abstract

fetched live from OpenAlex

Background: The patient and observer scar assessment scale (POSAS) recently emerged as a promising method, reflecting both observer's and patient's opinions in evaluating scar. This tool was shown to be consistent and reliable in burn scar assessment, but it has not been tested in the setting of skin graft scar in skin cancer patients. Objective: To evaluate facial skin graft scar applied to POSAS and to compare with objective scar assessment tools. Methods: Twenty three patients, who diagnosed with facial cutaneous malignancy and transplanted skin after Mohs micrographic surgery, were recruited. Observer assessment was performed by three independent rates using the observer component of the POSAS and Vancouver scar scale (VSS). Patient self-assessment was performed using the patient component of the POSAS. To quantify scar color and scar thickness more objectively, spectrophotometer and ultrasonography was applied. Results: Inter-observer reliability was substantial with both VSS and the observer component of the POSAS (average measure intraclass coefficient correlation, 0.76 and 0.80, respectively). The observer component consistently showed significant correlations with patients' ratings for the parameters of the POSAS (all p-values0.05). The correlation between subjective assessment using POSAS and objective assessment using spectrophotometer and ultrasonography showed low relationship. Conclusion: In facial skin graft scar assessment in skin cancer patients, the POSAS showed acceptable inter-observer reliability. This tool was more comprehensive and had higher correlation with patient's opinion. (

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.150
Threshold uncertainty score0.257

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.065
GPT teacher head0.400
Teacher spread0.335 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations88
Published2016
Admission routes1
Has abstractyes

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