Patient-Related Keloid Scar Assessment and Outcome Measures
Bibliographic record
Abstract
BACKGROUND: Keloid scars cause pain, itching, functional limitation, and disfigurement, leading to psychological distress. Progress in treatment regimens is hindered by the lack of a universally accepted outcome measure. The Patient and Observer Scar Assessment Scale is a tool for the assessment of scars, incorporating an assessment by both clinician and patient. This study evaluates its application to keloids and compares it to the widely used Vancouver Scar Scale, which is considered the standard mode of assessment for scars. METHODS: Three observers using the two scales assessed 34 patients with 41 keloid scars independently. Patients evaluated their own scars simultaneously using the patient component of the Patient and Observer Scar Assessment Scale. Internal consistency, interobserver reliability, and convergent validity were examined. RESULTS: Both components of the Patient and Observer Scar Assessment Scale had high internal consistency (0.82 and 0.86 for patient and observer components, respectively); those rates were higher than the rate for the Vancouver Scar Scale (0.65). Interobserver reliability was "substantial" for the Vancouver Scar Scale (0.65) and "almost perfect" for the observer component of the Patient and Observer Scar Assessment Scale (0.85). Convergent validity was very strong (0.83, p < 0.01), although the patient component did not correlate well with either of the observer scales. Patients rated their scars worse than the observer average for 83 percent of the scars, and were influenced by color, stiffness, thickness, and irregularity (p < 0.05). CONCLUSION: The findings support the use of the Patient and Observer Scar Assessment Scale as a reliable and valid method of assessing keloid scars in a clinical context. CLINICAL QUESTION/LEVEL OF EVIDENCE: Diagnostic, II.
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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".