14 SCAR-Q: An Update on Field-testing a Patient-reported Outcome Instrument for Burn, Surgical, and Traumatic Scars
Bibliographic record
Abstract
Millions of scars form annually from burns, surgery, and trauma. Scars have been shown to have wide-ranging effects on quality of life, including impaired physical and psychosocial functioning. Currently, there is no internationally validated, rigorously tested PRO instrument that can be used to assess scar outcomes of all etiologies in both children and adults. Our aim was to field-test the SCAR-Q in an international sample of patients. The ongoing international study involves hospitals in Canada, USA and New Zealand. Between March 31 to October 4, 2017 data were collected from 3 outpatient clinics in Auckland, New Zealand and Toronto, Canada. Participants were asked to complete a questionnaire booklet that asked demographic and clinical questions and the SCAR-Q (3 scales measuring appearance, symptoms, and scar-related psychosocial distress). Rasch Measurement Theory (RMT) analysis was conducted using RUMM2030 software to take an early look at SCAR-Q scales in terms of reliability (Person Separation Index, ‘PSI’), threshold for item response options (do response options such as ‘not at all’ perform as intended), and targeting (does the scale measure the construct as experienced by the sample). 408 patients were consented and 375 patients completed the survey results in full. The sample included 363(97%) adults and 156(42%) females. The scars were mainly not visible (n=195, 51%)and were caused by burns(n=86, 23%), surgeries(n=158, 42%), and trauma(n=131, 35%). All three scales had good to excellent reliability (PSI 0.78–0.89). Response options performance varied between scales. The Appearance Scale had no disordering of response options suggesting sequential integer scores increased for the construct measured. In terms of targeting, all three scales mapped out a clinical hierarchy for each concept of interest, providing support that the scales will work to measure clinical change. With the full dataset, RMT analysis will be conducted to select the best subset of items for each scale based on a range of statistical tests. SCAR-Q will be the first comprehensive PRO instrument for scar etiology (burn, traumatic, surgical) and for children (8 and older) and adults. The availability of a rigorously developed PRO instrument for scars will make it possible to include the patient’s perspective in clinical trials.
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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 | 0.035 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".