Translation of the Patient Scar Assessment Scale (PSAS) to French with cross-cultural adaptation, reliability evaluation and validation.
Bibliographic record
Abstract
BACKGROUND: Patient esthetic satisfaction related to scarring after orthopedic surgery was rarely assessed before the development of the Patient Scar Assessment Scale (PSAS). The purpose of our study was to translate and validate the PSAS and assess the psychometric properties of the French version. METHODS: We conducted a staged validation with forward and backward translation and concurrent validation. The validation committee comrpised bilingual experts. The patient validation sample comprised 53 orthopedic surgery patients who were assessed at a minimum of 1 year postoperatively. We followed a standardized process for cross-cultural adaptation to develop and assess the French version. First, 2 independent translators completed the forward translation of the PSAS and then met to achieve a consensus version. This consolidated version was then backward translated into English and cross-verified with the original version. A group of orthopedic and plastic surgeons assessed this version for content validity. We assessed the test-retest reliability of the new French scale, which was filled out twice by a cohort of 53 patients, using scale distribution analysis, internal consistency (Chronbach alpha) and absolute agreement (intraclass correlation coefficients [ICC (2,1)]). RESULTS: The level of agreement on the translation process between the translators initially and then later among the expert panel was high. The reliability of the translated version (PSAS-Fr) and its internal consistency was high (Chronback alpha 0.87-0.98 for each of the 6 questions), and the test-retest reliability was excellent (ICC 0.96). On the other hand, there was no bias between occasions (retests difference -0.24) and the scores fell within 2 standard deviations of 5. Older patients had higher satisfaction about scar appearance. CONCLUSION: The PSAS-Fr was successfully translated from the original English version and demonstrated strong cross-sectional psychometric properties. Further assessment in longitudinal studies is warranted.
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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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".