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Translation and adaptation of the Premature Infant Pain Profile into Brazilian Portuguese

2013· article· en· W2104824141 on OpenAlexaff
Mariana Bueno, Priscila Costa, Angélica Arantes Silva de Oliveira, Roberta Cardoso, Amélia Fumiko Kimura

Bibliographic record

VenueTexto & Contexto - Enfermagem · 2013
Typearticle
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsUniversity of Toronto
FundersFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsPortugueseBrazilian PortugueseEquivalence (formal languages)Semantic equivalenceInternationalizationPsychologyLinguisticsMedicineArtificial intelligenceComputer scienceBusinessSemantic Web

Abstract

fetched live from OpenAlex

The study aimed to translate and to adapt a version of the Premature Infant Pain Profile into the Brazilian Portuguese language. This is a cross-sectional and methodological study for the validation of a translated version of a tool. The process was conducted in four stages: initial translation, synthesis, back-translation, and analysis by experts. Four independent versions of the instrument translated into Brazilian Portuguese were produced. Based on these initial translations, a synthesis version was developed. Two back-translated versions were independently produced, and none showed major differences compared to the original instrument. An expert committee reviewed the summary version and the back-translations with respect to semantic and idiomatic equivalence. The committee considered the translation into Brazilian Portuguese as appropriate. Therefore, the Perfil de Dor no Recém-Nascido Pré-termo was considered adapted to Brazilian Portuguese, for research purposes and for clinical practice. It will contribute to the internationalization of research results in Brazil.

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 imitation

Not 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.

metaresearch head score (Codex)0.017
metaresearch head score (Gemma)0.035
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.091

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.035
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.151
GPT teacher head0.383
Teacher spread0.232 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations24
Published2013
Admission routes1
Has abstractyes

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