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Record W4391386439 · doi:10.55905/oelv22n1-222

Principais exames complementares analisados em pacientes com câncer de pâncreas

2024· article· pt· W4391386439 on OpenAlexaff
Adrielly Oliveira Mateus, Ariana Carneiro de Sousa Batista, Carmem Tainá Alves de Freitas, Nathália de Andrade Nery, Rafael Mesquita Soares, Leda Terezinha Freitas e Silva, Sávia Denise Silva Carlotto Herrera, Maykon Jhuly Martins de Paiva

Bibliographic record

VenueOBSERVATÓRIO DE LA ECONOMÍA LATINOAMERICANA · 2024
Typearticle
Languagept
FieldMedicine
TopicWomen's cancer prevention and management
Canadian institutionsPancreas Centre (Canada)
Fundersnot available
KeywordsMedicineGynecology

Abstract

fetched live from OpenAlex

O câncer de pâncreas representa uma preocupação médica significativa, especialmente devido ao seu prognóstico desfavorável, decorrente da falta de especificidade dos sintomas, da necessidade de múltiplos exames e, consequentemente, do diagnóstico tardio. Este artigo aborda a crucial compreensão da fisiopatologia do câncer pancreático e dos principais métodos diagnósticos. A metodologia empregada envolveu uma revisão literária integrativa, com consultas às bases de dados do Google Acadêmico, ScienceDirect e PubMed, utilizando palavras-chave como exames laboratoriais, câncer pancreático e diagnóstico. Os resultados destacam que o Adenocarcinoma Ductal Pancreático, em sua forma mais comum, exibe alta letalidade entre os afetados por essa enfermidade. O diagnóstico frequentemente requer a utilização de exames complementares para confirmar a hipótese diagnóstica, iniciar e monitorar o tratamento. Os marcadores tumorais CEA e CA 19-9 emergem como cruciais nos exames laboratoriais para o diagnóstico. Além disso, exames de imagem, como tomografia computadorizada, ultrassonografia e ressonância magnética, são destacados por suas vantagens e complementaridades específicas. A compreensão dos métodos de identificação dessa patologia surge como uma abordagem fundamental para melhorar a sobrevida dos pacientes com câncer de pâncreas.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.410
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0170.000

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 head0.026
GPT teacher head0.319
Teacher spread0.293 · 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
Published2024
Admission routes1
Has abstractyes

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