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Record W45857080 · doi:10.24095/hpcdp.31.2.04

Projection of future cancer incidence and new cancer cases in Manitoba, 2006–2025

2011· article· en· W45857080 on OpenAlexaffvenueabout
Janet Nowatzki, Bjørn Møller, A Demers

Bibliographic record

VenueChronic diseases in Canada · 2011
Typearticle
Languageen
FieldMedicine
TopicGlobal Cancer Incidence and Screening
Canadian institutionsUniversity of ManitobaCancerCare Manitoba
Fundersnot available
KeywordsMedicineCancer registryIncidence (geometry)Cancer incidenceCancerPopulationDemographyLung cancerProjections of population growthCancer preventionCohortEnvironmental healthGerontologyPopulation growthOncologyPathologyInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: Projecting the burden of cancer is important for evaluating prevention strategies and for administrative planning at cancer facilities. METHODS: We projected cancer incidence and counts for the population of Manitoba using population projections from the Manitoba Bureau of Statistics for the years 2006 to 2025 and cancer incidence data from the Manitoba Cancer Registry for the years 1976 to 2005. Data were analyzed using a version of the age-period-cohort model with recommended modifications that was developed and tested in the Nordic countries. RESULTS: The overall incidence of cancer in Manitoba is not projected to change substantially from 2006 to 2025. However, the age-standardized incidence for lung cancer is expected to decrease, particularly for males, highlighting the importance of tobacco prevention. The total number of new cancer cases per year is expected to increase 36% over the projection period, attributable primarily to demographic changes. CONCLUSION: As the population of Manitoba increases, resource and infrastructure planning will need to account for the expected increase in cancer cases.

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)0.000
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 score0.055
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.078
GPT teacher head0.320
Teacher spread0.242 · 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

Citations24
Published2011
Admission routes3
Has abstractyes

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