Scar Cosmesis: Assessment, Perception, and Impact on Body Image and Quality of Life—A Systematic Review
Bibliographic record
Abstract
Objectives: (1) Review current validated scar assessment tools and (2) describe the impact of scar cosmesis perception on body image and quality of life. Methods: Three independent reviewers performed comprehensive searches and identified 680 English language studies published between 1950 and 2014 (data sources: Medline, EMBASE, Cochrane Library, and Web of Science). Literature including case series, cross sectional studies, meta‐analyses, and reviews was then screened and selected according to strict inclusion/exclusion criteria. Results: Scar assessment: Review included Vancouver Scar Scale, Patient and Observer Scar Assessment Scale, Manchester Scar Scale, Wound Evaluation Scale, and Western Scar Index. Validated qualitative assessment tools were clinically more useful than their quantitative counterparts. Patient perception input increased validity. Subjective satisfaction rating had little correlation with objective assessment of scarring. Perceptions: The size of defect did not correlate with impact, however location and visibility did. Psychosocial distress correlated with subjective severity. The large impact on physical and psychosocial quality of life (ascertained by generic and symptom‐specific validated assessment tools, as well as qualitative studies with interpretive phenomenologic analysis) is not to be overlooked. Conclusions: Careful selection of scar assessment tools is vital to gauge severity and plan further treatment. No consensus exists on the single most appropriate tool. A validated assessment tool is important in the assessment of scarring. There is a tendency to underestimate and thereby worsen the impact of scarring on patients’ quality of life. Further studies are required, particularly in the context of thyroid surgery.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".