How to Assess Scar Quality in Pediatric Burn Patients: A Systematic Review on the Type and Content of Outcome Measurement Instruments
Bibliographic record
Abstract
Measuring scar quality is important for monitoring scar development and evaluating treatment outcomes. Given the substantial representation of children within the burn population and their susceptibility to lifelong scarring, evaluating scar quality is particularly important in this group. This study provides an overview of outcome measurement instruments used to assess scar quality in pediatric burn patients. A systematic literature search was conducted in PubMed, EMBASE and Web of Science covering studies published up to March 25, 2024. We included original research studies in English that measured at least one scar quality characteristic in pediatric burn patients. We included 328 studies and identified 585 outcome measurement instruments: clinician-reported outcome measures (CROMs) (53%), measurement devices (25%), and patient-reported outcome measures (PROMs) (22%). The most frequently used instruments were the (modified) Vancouver Scar Scale, ultrasound, and the Patient and Observer Scar Assessment Scale Patient scale, respectively. Thickness and itch were the most frequently assessed scar characteristics. The use of PROMs has increased over the past decade, particularly after 2016, highlighting their growing attention. Among the studies using PROMs, 42% reported age-related conditions, with thresholds for independent completion ranging from 5 to 16 years. However, CROMs are the most frequently used instruments. While PROMs, CROMs and measurement devices are valuable, they are often not specifically designed for or validated in pediatric burn patients, and therefore they could benefit from further development or validation to better address the specific needs of pediatric burn patients.
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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 | 0.016 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| 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".