Subjective Tools for Burn Scar Assessment: An Integrative Review
Bibliographic record
Abstract
OBJECTIVE: To review the clinical and scientific literature on the subjective ways of assessing burn scars and describe their main characteristics. DATA SOURCES: The Latin American, Caribbean Health Sciences Literature, Nursing Database, PubMed, CINAHL, and Scopus and Web of Science databases were used to search for studies published between 2014 and 2018 using descriptors in Portuguese, Spanish, and English. STUDY SELECTION: After establishing the research question and the location and definition of the studies, as well as accounting for differences among databases and application of filters based on inclusion and exclusion criteria, 886 references remained. DATA EXTRACTION: Investigators reviewed the titles and abstracts of the sample and selected 188 relevant studies for full review. DATA SYNTHESIS: Twenty-six subjective forms of assessment were found; most research concerned the Patient and Observer Scar Assessment Scale and the Vancouver Scar Scale. CONCLUSIONS: The Patient and Observer Scar Assessment Scale and the Vancouver Scar Scale are the most common scales for assessing burn scars and have similar evaluation points such as vascularization, pliability, pigmentation, and height, which are the main parameters that contribute to the general assessment and severity of a scar. There is a need to improve instructions for application of the scales to facilitate better understanding and improve agreement among evaluators.
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 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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.017 | 0.013 |
| 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) | 0.005 | 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".