Disease Severity and Quality of Life Outcome Measurements in Patients With Keloids: A Systematic Review
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".