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Record W4406155824 · doi:10.55905/cuadv17n1-043

Prevalência de alterações dermatológicas em gestantes do Município de Santarém – Pará

2025· article· pt· W4406155824 on OpenAlexaff
Luz Marina Silva Farias, Marina Nicolau Taketomi, Jorge Carlos Menezes Nascimento, Higson Rodrigues Coelho, Edna Ferreira Coêlho Galvão

Bibliographic record

VenueCuadernos de Educación y Desarrollo · 2025
Typearticle
Languagept
FieldHealth Professions
TopicNeonatal skin health care
Canadian institutionsContinental (Canada)
Fundersnot available
KeywordsMedicine

Abstract

fetched live from OpenAlex

A gravidez é um período de transformações fisiológicas, físicas e psicológicas na vida de uma mulher. Essas mudanças podem causar desequilíbrios hormonais e físicos, como alterações dermatológicas, que acabam afetando diretamente a autoestima da gestante. A pesquisa tem como objetivo analisar a prevalência das alterações dermatológicas mais percebidas pelas gestantes de Santarém-PA. Metodologicamente, esse trabalho é classificado como um estudo do tipo descritivo, transversal e de intervenção, com abordagem quantitativa. Por meio dos resultados, foi possível identificar que a maioria das gestantes apresenta alterações dermatológicas, podendo variar entre: estria, celulite, acne, hiperpigmentação, unhas quebradiças, queda de cabelo, Hirsutismo, rede haller e/ou tubérculos de Montgomery. Dentre essas, a hiperpigmentação foi a mais prevalente, seguida de estria e celulite. Os achados deste trabalho concordam com outras pesquisas, confirmam que modificações fisiológicas são comuns durante a gestação. Além disso, foi observada a escassez de cosméticos específicos para gestantes, destacando a necessidade de aprimoramento da indústria nesse setor. O conhecimento dessas alterações permite às gestantes e aos profissionais de saúde lidar melhor com essa problemática.

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)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.135
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.002
Science and technology studies0.0030.000
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0040.002

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 head0.030
GPT teacher head0.387
Teacher spread0.356 · 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; both teacher heads agree on what is shown here.

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
Published2025
Admission routes1
Has abstractyes

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