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Record W2081850477 · doi:10.1097/bcr.0b013e3182700054

Exploring Reliability of Scar Rating Scales Using Photographs of Burns From Children Aged up to 15 Years

2012· article· en· W2081850477 on OpenAlexaboutno aff
Megan Simons, Jenny Ziviani, Michelle Thorley, Jessamine McNee, Zephanie Tyack

Bibliographic record

VenueJournal of Burn Care & Research · 2012
Typearticle
Languageen
FieldMedicine
TopicBurn Injury Management and Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsInter-rater reliabilityMedicineScarsIntraclass correlationRating scaleReliability (semiconductor)Scale (ratio)SurgeryPsychologyPsychometricsClinical psychologyCartography

Abstract

fetched live from OpenAlex

Assessing burn scars from photographs is a common practice given the growing trend to support health service delivery via electronic media (eg, email, videoconferencing). Scar rating scales, originally designed for in-person assessment, have been used to rate scars from photographic images. Evidence for the reliability of this practice is lacking. Five raters completed three scar rating scales (Patient and Observer Scar Scale, Manchester Scar Scale, modified Vancouver Scar Scale), both in-person and using photographs on 12 participants (seven male, five female) with 18 scar areas (3 × 3 cm). Interrater reliability for the scar parameters of vascularity, color, contour, pliability, and overall opinion achieved intraclass correlation coefficient values of between 0.71 and 0.87 (in-person) and 0.72 and 0.77 (using photographs) for multiple raters. The level of agreement between in-person and photographic assessment was below acceptable levels, which brings into question construct validity when scar rating scales are used in a way for which they were not designed. Reliability estimates in this study were likely reduced by the underrepresentation of scars in the more severe range. This limitation needs to be addressed in future research. Advances are required in the development and refinement of burn scar rating scales, specifically for photographic use, given their routine use in clinical care.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.012
Threshold uncertainty score0.458

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.177
GPT teacher head0.405
Teacher spread0.227 · 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

Citations14
Published2012
Admission routes1
Has abstractyes

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