MétaCan
Menu
Back to cohort
Record W2508843118 · doi:10.3747/co.23.2906

Cancer Incidence, Mortality, and Stage at Diagnosis in First Nations Living in Manitoba

2016· article· en· W2508843118 on OpenAlexafffundvenueabout
Kathleen Decker, Erich V. Kliewer, A Demers, Katherine Fradette, Natalie Biswanger, Grace Musto, Brenda Elias, Donna Turner

Bibliographic record

VenueCurrent Oncology · 2016
Typearticle
Languageen
FieldMedicine
TopicGlobal Cancer Incidence and Screening
Canadian institutionsUniversity of ManitobaBC Cancer AgencyCancerCare Manitoba
FundersCanadian Institutes of Health Research
KeywordsMedicineCancerIncidence (geometry)Stage (stratigraphy)Cancer incidenceMortality rateDemographyGerontologyInternal medicineBiology

Abstract

fetched live from OpenAlex

BACKGROUND: In the present study, we examined breast (bca) and colorectal cancer (crc) incidence and mortality and stage at diagnosis for First Nations (fn) individuals and all other Manitobans (aoms). METHODS: Several population-based databases were linked to determine ethnicity and to calculate age-standardized incidence and mortality rates. Logistic regression was used to compare bca and crc stage at diagnosis. RESULTS: From 1984-1988 to 2004-2008, the incidence of bca increased for fn and aom women. Breast cancer mortality increased for fn women and decreased for aom women. First Nations women were significantly more likely than aom women to be diagnosed at stages iii-iv than at stage i [odds ratio (or) for women ≤50 years of age: 3.11; 95% confidence limits (cl): 1.20, 8.06; or for women 50-69 years of age: 1.72; 95% cl: 1.03, 2.88). The incidence and mortality of crc increased for fn individuals, but decreased for aoms. First Nations status was not significantly associated with crc stage at diagnosis (or for stages i-ii compared with stages iii-iv: 0.98; 95% cl: 0.68, 1.41; or for stages i-iii compared with stage iv: 0.91; 95% cl: 0.59, 1.40). CONCLUSIONS: Our results underscore the need for improved cancer screening participation and targeted initiatives that emphasis collaboration with fn communities to reduce barriers to screening and to promote healthy lifestyles.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.565
Threshold uncertainty score0.605

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.273
GPT teacher head0.465
Teacher spread0.193 · 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

Citations36
Published2016
Admission routes4
Has abstractyes

Explore more

Same venueCurrent OncologySame topicGlobal Cancer Incidence and ScreeningFrench-language works237,207