Validation of a New Device to Measure Postsurgical Scar Adherence
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Scarring after surgery can lead to a wide range of disorders. At present, the degree of scar adhesion is assessed manually and by ordinal scales. This article describes a new device (the Adheremeter) to measure scar adhesion and assesses its validity, reliability, and sensitivity to change. DESIGN: This was a reliability and validity study. SETTING: The study was conducted at the Scientific Institute of Veruno. PARTICIPANTS AND METHODS: Two independent raters, a physical therapist and a physical therapist student, used the Adheremeter to measure scar mobility and contralateral normal skin in a sample of 25 patients with adherent postsurgical scars before (T1) and after (T2) physical therapy. Two indexes of scar mobility, the adherence's surface mobility index (SM(A)) and the adherence severity index (AS), were calculated. Their correlation with the Vancouver Scar Scale (VSS) and its pliability subscale (PL-VSS) was assessed for the validity analysis. RESULTS: Both the SM(A) and the AS showed good-to-excellent intrarater reliability (intraclass correlation coefficient [ICC]=.96) and interrater reliability (SM(A): ICC=.97 and .99; AS: ICC=.87 and .87, respectively, at T1 and T2), correlated moderately with the VSS and PL-VSS only at T1 (r(s)=-.58 to -.66), and were able to detect changes (physical therapist/physical therapist student): z score=-4.09/-3.88 for the SM(A) and -4.32/-4.24 for the AS; effect size=0.6/0.4 for the SM(A) and 1.4/1.2 for the AS; standard error of measurement=4.59/4.79 mm(2) for the SM(A) and 0.05/0.06 for the AS; and minimum detectable change=12.68/13.23 mm(2) for the SM(A) and 0.14/0.17 for the AS. LIMITATIONS: The measurement is based on the rater's evaluation of force to stretch the skin and on the patient's judgment of comfort. DISCUSSION AND CONCLUSION: The Adheremeter showed a good level of reliability, validity, and sensitivity to change. Further studies are needed to confirm these results in larger cohorts and to assess the device's validity for other types of scars.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".