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Influencia del lugar de origen en la utilización de pruebas de cribado de cáncer ginecológico en España

2011· article· es· W2034095557 on OpenAlexaboutno aff
Belén Sanz‐Barbero, Enrique Regidor, Silvia Galindo

Bibliographic record

VenueRevista de Saúde Pública · 2011
Typearticle
Languagees
FieldMedicine
TopicGlobal Cancer Incidence and Screening
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineConfidence intervalGynecologyOdds ratioSocioeconomic statusDemographyLogistic regressionCervical cancerPopulationCervical cancer screeningCancerEnvironmental healthInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: To assess the association between geographic origin and the use of screening cervical smears and mammograms. METHODS: Data was obtained from the 2006 Spanish National Health Survey that included 13,422 females over 16 years of age. The dependent variable was use of screening mammograms and cervical smears in the past 12 months. The measure of association (odds ratio and its related 95% confidence interval) was estimated using logistic regression. RESULTS: African women were 0.36 (95% CI 0.21,0.62), Eastern European 0.40 (95%CI 0.22;0.74), Western European, American and Canadian 0.60 (95%CI 0.43,0.84), and Central and South American 0.64 times (95%CI 0.52, 0.81) less likely to undergo a mammogram compared with the general population of Spain. In regard to cervical cancer screening, Eastern European women were 0.38 (95%CI 0.28,0.50), African 0.47 (95%CI 0.33,0.67) and Western European, American and Canadian 0.61 times (95%CI 0.46, 0.81) less likely to undergo cervical smears. These associations were independent of age, socioeconomic condition, health status and health insurance coverage. CONCLUSIONS: Immigrant women use less screening programs than native Spanish women. This finding may suggest difficult access to prevention programs.

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)0.004
metaresearch head score (Gemma)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.426
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.052
GPT teacher head0.338
Teacher spread0.286 · 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

Citations23
Published2011
Admission routes1
Has abstractyes

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