Systematic review on the content of outcome measurement instruments on scar quality
Bibliographic record
Abstract
Background: Measurements of scar quality are essential to evaluate the effectiveness of scar treatments and to monitor scars. A large number of scar scales and measurement devices have been developed, which makes instrument selection challenging. The aim of this study was to provide an overview of the content (ie, included items) of all outcome measurement instruments that measure scar quality in different types of scars (burn, surgical, keloid, and necrotizing fasciitis), and the frequency at which the instruments and included items are used. Methods: A systematic search was performed in PubMed and Embase.com up to October 31, 2018. All original studies reporting on instruments that measured at least 1 characteristic of scar quality were included and the instrument’s content was extracted. Results: We included 440 studies for data extraction. Included instruments (N = 909) were clinician-reported scales (41%), measurement devices (30%), patientreported scales (26%), and combined clinician- and patient-reported scales (3%). The Observer scale of the Patient and Observer Scar Assessment Scale, the Cutometer, the Patient Scale of the Patient and Observer Scar Assessment Scale, and the modified Vancouver Scar Scale were the most often used instrument in each of these categories, respectively. The most frequent assessed items were thickness, vascularity, pigmentation, pliability, pain, and itch. Conclusion: The results of this study lay the foundation for our future research, which includes an international Delphi study among many scar experts, and an international focus group study among scar patients, aiming to elucidate how scar quality must be defined and measured from both professional and patient perspectives.
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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 | 0.037 | 0.222 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.009 |
| Bibliometrics | 0.017 | 0.020 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".