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Record W2987294913 · doi:10.24095/hpcdp.39.11.04

At-a-glance - Cancer trends in Canada, 1984 to 2015

2019· article· en· W2987294913 on OpenAlexaffvenueabout
Alain Demers, Darren R. Brenner, Leah Smith, Amanda Shaw

Bibliographic record

VenueHealth Promotion and Chronic Disease Prevention in Canada · 2019
Typearticle
Languageen
FieldMedicine
TopicGlobal Cancer Incidence and Screening
Canadian institutionsCanadian Cancer SocietyUniversity of CalgaryUniversity of ManitobaPublic Health Agency of Canada
Fundersnot available
KeywordsCancerGeographyRegional scienceHistoryPolitical scienceMedicineInternal medicine

Abstract

fetched live from OpenAlex

Examining incidence trends of all cancers combined in order to understand cancer trends can be misleading, as patterns can vary across individual cancer types. This paper highlights findings on trends over time from Canadian Cancer Statistics 2019, as measured by the annual percent change (APC) of age-standardized incidence rates. Among the results were a recent increase in thyroid cancer in males (APC: 6.4%, 1997-2015), as well as decreases in prostate cancer (APC: -9.1%, 2011-2015) and cervical cancer (APC: -3.3%, 2010-2015).

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.035
Threshold uncertainty score0.254

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0040.011
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.052
GPT teacher head0.381
Teacher spread0.329 · 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

Citations4
Published2019
Admission routes3
Has abstractyes

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