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Record W3086789079 · doi:10.5539/gjhs.v12n11p45

Environment as a Breast Cancer-Causing Factors in Urban Women in Indonesia

2020· article· en· W3086789079 on OpenAlexvenueno aff
Ika Dharmayanti, Khadijah Azhar, Dwi Hapsari Tjandrarini

Bibliographic record

VenueGlobal Journal of Health Science · 2020
Typearticle
Languageen
FieldMedicine
TopicCancer Risks and Factors
Canadian institutionsnot available
Fundersnot available
KeywordsBreast cancerIncidence (geometry)MedicineEnvironmental healthCancerBreast cancer awarenessGerontologyDemographyInternal medicine

Abstract

fetched live from OpenAlex

Environmental influence is one of the important factors in breast cancer incidence because residential and work environments may be potential for breast cancer outcomes. This study aimed to determine residential and workplace environments with the occurrence of women breast cancer in urban areas in Indonesia. This study used data from Non-Communicable Disease (NCD Research) in 2016 which covered 34 provinces in Indonesia. There were 38,790 people to measure the occurrence of breast cancer in women aged 25–64 years. The sample was women who were willing to be interviewed and to conduct clinical breast examinations (Sadanis). The analysis was aimed at finding out the correlation between exposure, residential environment, and workplaces with the occurrence of breast tumor/cancer. The results showed that the risk of breast cancer in women who worked in risky workplaces from the normal state was 1.96 times higher than women who worked in non-risky workplaces (OR=1.96; 95% CI= 1.41 to 2.7; p<0.001). Suspected of tumor/cancer in the residential areas are inversely proportional to those not living close to the mining location (OR=0.86; 95% CI= 0.77 to 0.97; p<0.001). These findings suggest the important role of the environment in breast cancer incidence. Therefore, it is recommended to apply a healthy lifestyle, both physically and spiritually, and provide regular health screening.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.950

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.027
GPT teacher head0.331
Teacher spread0.304 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2020
Admission routes1
Has abstractyes

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