Clinimetric properties and clinical utility in rehabilitation of postsurgical scar rating scales
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it