Cross‐cultural adaptation and psychometric validation of the Patient Scar Assessment Questionnaire to the Spanish language in head and neck surgery
Bibliographic record
Abstract
External appearance is the main aesthetic outcome in patients who undergo surgical procedures. Scars located in exposed areas, such as the neck and face, are important for patients. There are at least eight instruments that are used to evaluate postoperative scars, but few fulfil standard methodological conditions. The Patient Scar Assessment Questionnaire (PSAQ) was designed and validated using psychometric methodology. However, this scale has not been translated or validated in the Spanish language. The aim of this study was to undergo a cross-cultural adaptation and psychometric validation of the PSAQ scale to the Spanish language in patients who underwent head and neck surgery. We followed The Professional Society for Health Economics and Outcomes Research (ISPOR) guidelines for the translation and validation of health-related scales. Forward and back translations were made by independent translators. We included adult patients who underwent thyroidectomy, parathyroidectomy, parotidectomy, and neck dissection. For the psychometric validation, we used a principal axis exploratory factor analysis with oblimin rotation. A reliability test involving Cronbachs alpha and the item-total correlation was performed and for the convergent/concurrent validity, we selected the Spanish version of the Vancouver Scar Scale. A total of 180 patients were recruited. Factor analysis showed a five-factor solution. Cronbachs alpha for the subscales was >0.7. The comparison between the PSAQ appearance subscale and the VSS demonstrated a high correlation (rho = - 0.89). In a sample of 62 patients, the test-retest evaluation showed high correlation (0.74-0.99). Our study supports the Spanish version of the PSAQ as a valid, reliable, and reproducible tool to assess the perception and impact of neck scars in Spanish-speaking patients who undergo head and neck surgery.
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".