The Patient and Observe Scar Assessment Scale (POSAS): translation into Portuguese, cultural adaptation and validation
Bibliographic record
Abstract
The Patient And Observe Scar Assessment Scale (POSAS) is one of the most robust instruments to evaluate the quality of the scar, but there is no validated version for Brazilian Portuguese. Objective: Translate and validate a Brazilian version of POSAS (POSAS EPM/UNIFESP). Methods: POSASv2.0 has been culturally adapted to international standards. The psychometric evaluation included acceptability / viability, internal consistency, reproducibility, construction validity and sensitivity to change. Results: The cultural equivalence of POSAS EPM/UNIFESP with the English version was confirmed. The validation of the study included 35 individuals with surgical scars and 35 medical specialists. Both subscales showed strong internal consistency (Cronbach's α = 0.77-0.93). Reproducibility was excellent and significant intra- and inter-observer (r> 0.9) (p <0.05). The POSAS Observer and Patient scales showed reproducibility greater than five points in all items (Cronbach's α> 0.5). The validity of the construct was significant and showed good sensitivity between POSAS EPM/UNIFESP and Vancouver Scar Scale (VSS). Conclusion: POSAS EPM/UNIFESP can be used to evaluate patients with surgical scars in the Brazilian population. If proved to be useful for clinical and research purposes, it should be used to capture medical opinions and the patients themselves.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.004 | 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".