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Record W4283797676 · doi:10.53660/clm-333-339

Laudos de mamografia de rastreamento no Brasil, uma análise da cobertura e dos indicadores socioeconômicos

2022· article· pt· W4283797676 on OpenAlexaff
Bárbara Rhayane Santos, Paulo Henrique Freire Ribeiro de Santana, João Eduardo Andrade Tavares de Aguiar, Thaís Serafim Leite de Barros Silva, Júlia Maria Gonçalves Dias

Bibliographic record

VenueConcilium · 2022
Typearticle
Languagept
FieldMedicine
TopicWomen's cancer prevention and management
Canadian institutionsDiscovery Air (Canada)
Fundersnot available
KeywordsGynecologyHumanitiesPolitical scienceMedicine

Abstract

fetched live from OpenAlex

O objetivo do estudo é avaliar a cobertura da mamografia (MMG) de rastreio das macrorregiões do Brasil, e a correlação dos laudos mamográficos com os indicadores socioeconômicos estaduais. Trata-se de um estudo retrospectivo, do tipo ecológico que analisa os laudos das MMG de rastreio no Brasil, de mulheres entre 50 e 69 anos, dos anos de 2013 a 2020. Os dados foram obtidos do Sistema de Informação do Câncer (SISCAN), plataforma DATASUS, e organizados em um banco de dados no programa Excel. Os resultados mostraram uma cobertura nacional de 12,07%, no período estudado, o sul obteve a maior cobertura dentre as macrorregiões, de 17,07%, seguido do nordeste, com 12,56%. O coeficiente de correlação de Pearson entre o percentual de laudos suspeitos de câncer de mama e o Índice de Desenvolvimento Humano Municipal (IDHM) foi - 0,0349. Como principais conclusões temos que o rastreio está aquém das metas e sua cobertura é desigual entre as macrorregiões. A correlação nula, pode estar relacionada aos números pouco expressivos de exames realizados e as limitações dos indicadores.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.073
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0360.001

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 head0.015
GPT teacher head0.282
Teacher spread0.267 · 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 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
Published2022
Admission routes1
Has abstractyes

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