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Record W2488279329

New Atrophic Acne Scar Classification: Reliability of Assessments Based on Size, Shape, and Number.

2016· article· en· W2488279329 on OpenAlexaff
Sewon Kang, Vicente Torres Lozada, Vincenzo Bettoli, Jerry Tan, María José Rueda, Alison Layton, Laurent Petit, Brigitte Dréno

Bibliographic record

VenuePubMed · 2016
Typearticle
Languageen
FieldMedicine
TopicDermatologic Treatments and Research
Canadian institutionsWestern University
Fundersnot available
KeywordsMedicineScarsAcne scarsDermatologyAcneReliability (semiconductor)Surgery
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: Post-acne atrophic scarring is a major concern for which standardized outcome measures are needed. Traditionally, this type of scar has been classified based on shape; but survey of practicing dermatologists has shown that atrophic scar morphology has not been well enough defined to allow good agreement in clinical classification. Reliance on clinical assessment is still needed at the current time, since objective tools are not yet available in routine practice.<br/> OBJECTIVES: Evaluate classification for atrophic acne scars by shape, size, and facial location and establish reliability in assessments.<br/> METHODS: We conducted a non-interventional study with dermatologists performing live clinical assessments of atrophic acne scars. To objectively compare identification of lesions, individual lesions were marked on a high-resolution photo of the patient that was displayed on a computer during the clinical evaluation. The Jacob clinical classification system was used to define three primary shapes of scars 1) icepick, 2) boxcar, and 3) rolling. To determine agreement for classification by size, independent technicians assessed the investigators' markings on digital images. Identical localization of scars was denoted if the maximal distance between their centers was &le; 60 pixels (approximately 3 mm). Raters assessed scars on the same patients twice (morning/afternoon). Aggregate models of rater assessments were created and analyzed for agreement.<br/> RESULTS: Raters counted a mean scar count per subject ranging from 15.75 to 40.25 scars. Approximately 50% of scars were identified by all raters and ~75% of scars were identified by at least 2 of 3 raters (weak agreement, Kappa pairwise agreement 0.30). Agreement between consecutive counts was moderate, with Kappa index ranging from 0.26 to 0.47 (after exclusion of one outlier investigator who had significantly higher counts than all others). Shape classifications of icepick, boxcar, and rolling differed significantly between raters and even for same raters at consecutive sessions (P&lt;.001 and P=0.4, respectively). Analysis showed only 65% of scars were identical in both sessions. We also found that there is a threshold of detection in terms of size, with poor agreement among investigators for very small scars (&lt;2 mm). The repeatability of identification of scars &ge; 2.0 mm was acceptable, and we found that increasing scar size was positively correlated with agreement. Reliability was improved when only scars &gt;2 mm were included. For smaller scars (&lt;2 mm), inter-rater reliability was poor.<br/> CONCLUSIONS: While intuitively it makes sense that describing scar morphology could guide treatment, we have shown that shape-based evaluations are subjective and do not readily yield strong agreement. Until there is a more objective way to evaluate morphology that is readily available to practicing clinicians, we propose that size should be considered a primary characteristic for scar classification systems. We further suggest classification of &lt;2 mm, 2-4 mm, and &gt;4 mm based on how the size would likely affect diagnostic and therapeutic choices. Finally, we recommend that scars &lt;2 mm not be included in a clinical classification but should be evaluated by an objective method that may be refined in the future. <br /><br /> <em>J Drugs Dermatol. </em>2016;15(6):693-702.

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 score0.362
Threshold uncertainty score0.381

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 head0.051
GPT teacher head0.333
Teacher spread0.282 · 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

Citations21
Published2016
Admission routes1
Has abstractyes

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