Clinimetric properties and clinical utility in rehabilitation of postsurgical scar rating scales
Bibliographic record
Abstract
The aim of this study was to review and critically assess the most used and clinimetrically sound outcome measures currently available for postsurgical scar assessment in rehabilitation. We performed a systematic review of the Medline and Embase databases to June 2015. All published peer-reviewed studies referring to the development, validation, or clinical use of scales or questionnaires in patients with linear scars were screened. Of 922 articles initially identified in the literature search, 48 full-text articles were retrieved for assessment. Of these, 16 fulfilled the inclusion criteria for data collection. Data were collected pertaining to instrument item domains, validity, reliability, and Rasch analysis. The eight outcome measures identified were as follows: Vancouver Scar Scale, Dermatology Life Quality Index, Manchester Scar Scale, Patient and Observer Scar Assessment Scale, Bock Quality of Life (Bock QoL) questionnaire, Stony Brook Scar Evaluation Scale, Patient-Reported Impact of Scars Measure, and Patient Scar Assessment Questionnaire. Scales were examined for their clinimetric properties, and recommendations for their clinical or research use and selection were made. There is currently no absolute gold standard to be used in rehabilitation for the assessment of postsurgical scars, although the Patient and Observer Scar Assessment Scale and the Patient-Reported Impact of Scars Measure emerged as the most robust scales.
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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 | 0.036 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.016 | 0.012 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".