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Record W7120288841

The Patient and Observe Scar Assessment Scale (POSAS): translation into Portuguese, cultural adaptation and validation

2018· article· pt· W7120288841 on OpenAlexaboutno aff
Luiz Guilherme De Saboya [UNIFESP] Lenzi

Bibliographic record

VenueUNIFESP Institutional Repository (Universidade Federal de São Paulo) · 2018
Typearticle
Languagept
FieldMedicine
TopicDermatologic Treatments and Research
Canadian institutionsnot available
Fundersnot available
KeywordsScale (ratio)Construct validityInternal consistencyReproducibilityScarsEquivalence (formal languages)PsychometricsAdaptation (eye)Consistency (knowledge bases)
DOInot available

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.545
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.031
GPT teacher head0.291
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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