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Record W3089316192 · doi:10.25248/reas.e3998.2020

O uso da tomografia computadorizada de baixa dose (TCBD) no rastreio de câncer de pulmão: revisão narrativa

2020· article· pt· W3089316192 on OpenAlexaff
Laerte de Paiva Viana Filho, João Pedro Costa Apolinário, Daniela Teixeira Ribeiro, Gabriele Maria Braga, Laura de Araújo Soares, Pedro Paulo Teixeira Baraky, Yllana Ferreira Alves Da Silva

Bibliographic record

VenueRevista Eletrônica Acervo Saúde · 2020
Typearticle
Languagept
FieldMedicine
TopicPalliative and Oncologic Care
Canadian institutionsDiscovery Air (Canada)
Fundersnot available
KeywordsMedicineGynecology

Abstract

fetched live from OpenAlex

Objetivo: Avaliar o uso da tomografia computadorizada de baixa dose (TCBD) no rastreamento do câncer de pulmão (CP). Revisão bibliográfica: O CP representa a neoplasia com maior incidência e mortalidade no mundo e caracteriza-se por rápida progressão e diagnóstico tardio, fatores que limitam as opções terapêuticas e contribuem para a baixa sobrevida dos pacientes. Medidas de rastreio mostram-se favoráveis em pacientes assintomáticos com alto risco de neoplasia, contribuindo com um prognóstico favorável. A TCBD revela-se como a principal ferramenta de rastreio, apresentando maior sensibilidade na detecção precoce de neoplasia de pulmão e de câncer em estágio I, quando comparada à triagem por radiografia torácica. Entretanto, fatores como a relação custo-benefício, a alta exposição à radiação ionizante e risco de falsos positivos representam empecilhos que devem ser analisados e considerados antes da implementação da triagem generalizada por TCBD. Considerações finais: A TCBD é objeto de estudo devido à grande relevância de seu uso na triagem de câncer de pulmão, decorrente de análises bibliográficas e pesquisas anteriores.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.289
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.002

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 head0.052
GPT teacher head0.333
Teacher spread0.281 · 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 designNot applicable
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
Published2020
Admission routes1
Has abstractyes

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