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Record W2338325819 · doi:10.1158/1538-7755.disp15-c18

Abstract C18: The burden of cancer in indigenous people globally and the World Indigenous Cancer Conference 2016 (WICC16)

2016· article· en· W2338325819 on OpenAlexaboutno aff
Suzanne Moore, Bronwyn Morris, Joan Cunningham, Gail Garvey

Bibliographic record

VenueCancer Epidemiology Biomarkers & Prevention · 2016
Typearticle
Languageen
FieldMedicine
TopicGlobal Cancer Incidence and Screening
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousPopulationCancerPublic healthMedicineDemographyGerontologyEnvironmental healthSociologyPathologyBiology

Abstract

fetched live from OpenAlex

Abstract Introduction: Cancer prognosis for Indigenous peoples, who represented 6% of the world population, is largely unknown. The rights of indigenous people, including their right to equality in health, have become a focus of the U.N. and other agencies, but little attention has been given to their cancer burden. Data from countries such as Australia, New Zealand Canada, and the United States, which have relatively well resourced cancer registries, show that cancer is now the second leading cause of death among Indigenous people in those countries. Greater understanding of the burden of cancer among indigenous populations is of major importance to public health, given that poorer outcomes contribute to the lower life expectancies. Methods: We conducted an overview of the literature surrounding cancer burden, including screening, epidemiology, treatment and survivor-ship among indigenous people globally. Results: Globally, the research work has been piecemeal and focused on the colonized inhabitants of the four former British or French colonies with higher Human Development Indexes (HDI), namely Australia, New Zealand, Canada and the United States (U.S.). Overall, cancer incidence and mortality among Indigenous Australians is greater and survival is significantly poorer than among non-Indigenous Australians, as is the case among Maori in New Zealand. First Nations people in Ontario are reported to have poorer survival for breast, prostate, cervical, colorectal (male and female) and male lung cancers compared to their non-First Nations peers and cancer mortality is higher for both American Indian and Alaska Native than for U.S. Whites. Maori women and Indigenous Australian women are less likely to participate in National Screening Programs; the rate of mammography in the latter group are 36% for Indigenous women and 55% for non-Indigenous women and rates of cervical screening in Maori women are also lower. There has been a dearth of research into the cancer burden of Indigenous people in other regions. The dearth of information, and lack of international collaboration, highlighted the need for an international multidisciplinary meeting, inviting participation from researchers, public health practitioners, clinicians, nurses, advocacy groups, allied health professionals, and related professionals from around the globe. The World Indigenous Cancer Conference 2016 will provide opportunities to foster new collaboration, enhance capacity and increase knowledge about cancer and Indigenous people nationally and internationally. Networks and partnerships can bring together the combined expertise and efforts of many to focus on the critical challenge of cancer among Indigenous peoples. By enabling those who work in or are interested in this area to connect, communicate and collaborate, this conference will encourage high quality cancer research and partnerships across the spectrum of cancer among indigenous people: from prevention, to psycho-social and health services research aimed at improving the quality of life for survivors, their family and friends. The conference will contain a number of streams including ‘Screening and Prevention’, ‘Cancer Data’, ‘Health Services’, ‘Psycho-social/survivorship’ and ‘Advocacy and capacity building’ Conclusion: Establishing networks and partnerships is an important method to raising the profile and addressing cancer disparities. Our aim is to overcome inequalities in cancer care for Indigenous peoples through the establishment of international collaborations of researchers with experience in areas such as epidemiology of chronic disease, social determinants of health, and health systems research. Citation Format: Suzanne P. Moore, Bronwyn Morris, Joan Cunningham, Gail Garvey. The burden of cancer in indigenous people globally and the World Indigenous Cancer Conference 2016 (WICC16). [abstract]. In: Proceedings of the Eighth AACR Conference on The Science of Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; Nov 13-16, 2015; Atlanta, GA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2016;25(3 Suppl):Abstract nr C18.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Review
About the Canadian research system: no · About a Canadian topic: yes
Systematic reviewhigh
gptno category
Domain: not available · Genre: Review
About the Canadian research system: no · About a Canadian topic: yes
Other designhigh
models splitAgreement compares identical category sets and study designs across arms.

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.186
Threshold uncertainty score0.369

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0020.001
Scholarly communication0.0030.001
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0280.004

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 head0.066
GPT teacher head0.392
Teacher spread0.326 · 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

Labeled directly by 2 models reading the full record.

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

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designSystematic review · Other design
Domainnot available
GenreReview

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
Published2016
Admission routes1
Has abstractyes

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