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Internações Sensíveis no SUS (2010–2025): Análise Crítica dos Determinantes Estruturais na Atenção Primária

2025· article· W4416819025 on OpenAlexaboutno aff
Luiza De Holanda becalli, Rafaela Nunes crispino, Matheus Bassalo aflalo, Antônio Maria Sousa Amorim filho, Ângelo Ceccon Duarte taboni, Carolynne Lima de Sousa, Cristiana Santana age burlamaqui, Manuela E. Gomes, Ingrid Pires segato

Bibliographic record

VenueBrazilian Journal of Implantology and Health Sciences · 2025
Typearticle
Language
FieldHealth Professions
TopicHealth, Nursing, Elderly Care
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)Work (physics)Scope (computer science)Population

Abstract

fetched live from OpenAlex

INTRODUÇÃO: As Internações por Condições Sensíveis à Atenção Primária (ICSAP) são um indicador-chave da efetividade da Atenção Primária à Saúde (APS) no Sistema Único de Saúde (SUS). O desempenho da APS na redução dessas internações é modulado por fatores estruturais, como modelos de financiamento, a gestão da carga de doenças crônicas e as profundas desigualdades sociais e territoriais. OBJETIVOS: Analisar criticamente a produção científica longitudinal (período 2010–2025) sobre a associação entre o desempenho da APS, mensurado pela redução de ICSAP, e os fatores estruturais do SUS. METODOLOGIA: Realizou-se uma revisão de literatura com síntese narrativa estruturada, com buscas nas bases PubMed, Scopus, Embase e Portal CAPES. Foram incluídos estudos longitudinais (painel, coorte, séries temporais) e quasi-experimentais (DiD, ITS) focados no SUS, publicados entre 2010 e 2025. A qualidade metodológica foi avaliada pelos critérios Newcastle-Ottawa (NOS) e a certeza da evidência pelo framework GRADE. CONCLUSÃO: Conclui-se que a APS é um investimento robusto, mas sua sustentabilidade depende de financiamento estável, priorização da qualificação (MFC) e proteção contra a austeridade, cujos piores impactos nas doenças crônicas ainda podem estar por se manifestar.

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)0.010
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.450
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0020.001
Science and technology studies0.0060.003
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0010.000

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 head0.075
GPT teacher head0.444
Teacher spread0.369 · 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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