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Record W4319053943 · doi:10.24095/hpcdp.35.s1.02

Cancer incidence in Canada: trends and projections (1983–2032)

2015· article· en· W4319053943 on OpenAlexaffvenueabout
Lin Xie, R Semenciw, Les Mery

Bibliographic record

VenueHealth Promotion and Chronic Disease Prevention in Canada · 2015
Typearticle
Languageen
FieldMedicine
TopicGlobal Cancer Incidence and Screening
Canadian institutionsPublic Health Agency of Canada
Fundersnot available
KeywordsCancer incidencePublic healthCancerPsychological interventionIncidence (geometry)Economic growthPolitical scienceMedicineEconomics

Abstract

fetched live from OpenAlex

Each year, the Canadian Cancer Statistics publication provides an estimate of expected case counts and rates for common cancer sites for the current year in Canada as a whole and in the provinces and territories. This monograph expands on the Canadian Cancer Statistics publication by providing historical and projected cancer incidence frequencies and rates at national and regional levels from 1983 to 2032. The aim is that this monograph will be an important resource for health researchers and planners. Most importantly, it is hoped the monograph will: - provide evidence-based input for the development of public health policy priorities at the regional and national levels; and - guide public health officials in planning strategy including designing and evaluating preventive interventions and planning resources (treatment requirements) and infrastructure for future cancer control and care intended to reduce the burden of cancer in Canada.

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 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)0.001
metaresearch head score (Gemma)0.002
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 score0.040
Threshold uncertainty score0.291

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0040.011
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.004

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 head0.102
GPT teacher head0.394
Teacher spread0.292 · 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 source (direct Gemma or distilled Codex), 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

Citations72
Published2015
Admission routes3
Has abstractyes

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