Cross‐cultural adaptation, reproducibility and validation of the Italian version of the Patient and Observer Scar Assessment Scale (<scp>POSAS</scp>)
Bibliographic record
Abstract
ABSTRACT The Patient and Observer Scar Assessment Scale (POSAS) is one of the most robust instruments to assess scar quality, but there is no Italian version, and no other competing instruments are available in Italian. The aim of this study was to translate and validate an Italian version of POSAS (POSAS‐I). POSASv2.0 was culturally adapted in accordance with international standards. The psychometric assessment included acceptability/feasibility, internal consistency, reproducibility, construct validity and sensitivity to change. Cultural equivalence of POSAS‐I with the English version was confirmed. The validation study included 102 subjects with surgical scars. Both subscales demonstrated acceptable internal consistency (Cronbach's α = 0·72–0·80). Reproducibility of the OSAS‐I (ICCs = 0·93–0·94; SEM = 1·8 points; MDC95 = 5·1 points) was superior to that of PSAS‐I (ICC = 0·65; SEM = 5·7 points; MDC95 = 15·7 points). OSAS‐I showed moderate to good correlations with the Vancouver Scar Scale (VSS), Global Rating of Change Scale (GRCS) and PSAS‐I. Sensitivity to change was large for PSAS‐I (effect size = 1·08; standardised response mean = 0·96) and moderate to large for OSAS‐I (ES = 0·69; SRM = 0·92). This study confirmed that POSAS‐I can be used to assess patients with surgical scars in the Italian population. OSAS‐I is useful for clinical and research purposes, while PSAS‐I should be better used to capture patients' own opinions and symptoms in clinical settings.
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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 | 0.026 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".