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Record W2977133933 · doi:10.1097/dss.0000000000002172

Disease Severity and Quality of Life Outcome Measurements in Patients With Keloids: A Systematic Review

2019· review· en· W2977133933 on OpenAlexaboutno aff
Alexis B. Lyons, Anjelica Peacock, Taylor L. Braunberger, Kate V. Viola, David Ozog

Bibliographic record

VenueDermatologic Surgery · 2019
Typereview
Languageen
FieldMedicine
TopicDermatologic Treatments and Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineKeloidMEDLINERandomized controlled trialQuality of life (healthcare)Gold standard (test)DiseaseClinical trialPhysical therapySystematic reviewSeverity of illnessSurgeryInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Keloids have been assessed by numerous methods and severity indices resulting in a lack of standardization across published research. OBJECTIVE: This study aims to evaluate published keloid randomized controlled trials (RCTs) and identify the need for a gold standard of assessment. METHODS AND MATERIALS: PubMed, MEDLINE, and Embase were searched for human RCTs on keloid treatment during a 10-year period. Eligible studies were English language RCTs reporting disease severity outcome measures after keloid treatments. RESULTS: A total of 40 disease outcome measures were used in 41 included RCTs. Twenty-four (59%) of the included studies used more than one disease severity scale. The most frequently used outcome measures were the Vancouver Scar Scale (34%) (n = 14), followed by serial photography (24%) (n = 10). These were followed by adverse events and complications (20%) (n = 8), Visual Analogue Scale (12%) (n = 5), keloid dimensions (12%) (n = 5), and Patient and Observer Scar Assessment Scale (10%) (n = 4). Only one study reported quality of life outcomes. CONCLUSION: There is wide variation in keloid outcome measures in the published literature. A standardized method of assessment should be implemented to reduce the disparities between studies and to better be able to compare the numerous treatment modalities.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.236
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0100.001
Bibliometrics0.0000.001
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 head0.282
GPT teacher head0.408
Teacher spread0.126 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations11
Published2019
Admission routes1
Has abstractyes

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