MétaCan
Menu
Back to cohort
Record W2101224482

Trends in cancer prevalence in Quebec.

2006· article· en· W2101224482 on OpenAlexaffabout
R. Louchini, Michel Beaupré, Alain Demers, Patricia Goggin, Clermont Bouchard

Bibliographic record

VenuePubMed · 2006
Typearticle
Languageen
FieldMedicine
TopicCancer Risks and Factors
Canadian institutionsInstitut National de Santé Publique du Québec
Fundersnot available
KeywordsMedicineCancerCancer registryEpidemiologyPublic healthBreast cancerDiseaseLung cancerMalignancyBladder cancerPrevalenceDemographyInternal medicinePathology
DOInot available

Abstract

fetched live from OpenAlex

Cancer prevalence is of prime interest in public health because of its use in estimating the disease's burden on the heath care system. This study's objective was to estimate five-year prevalence of tumours from 1989 to 1999 and ten-year prevalence of tumours from 1994 to 1999 in the Province of Quebec (Canada). Five-year prevalence was used to represent tumours for which people are more likely to obtain primary treatment; ten-year prevalence included those tumours in addition to tumours that can be considered cured but still need follow-up. Information was extracted from the Quebec Cancer Registry. Prostate cancer was the most prevalent malignancy among males (25 percent, five-year prevalent tumours), while breast cancer was most prevalent among females (38 percent, five-year prevalent tumours). For both sexes, the greatest observed prevalence increase was for endocrine glands. On average, five-year prevalence proportions were 16 percent higher in men than in women; those of ten year were 14 percent higher in men. Furthermore, the largest differences were observed for bladder and lung cancer. The change in cancer prevalence in Quebec was dependent on the tumour site.

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 score0.323
Threshold uncertainty score0.708

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 head0.026
GPT teacher head0.283
Teacher spread0.257 · 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

Citations7
Published2006
Admission routes2
Has abstractyes

Explore more

Same venuePubMedSame topicCancer Risks and FactorsFrench-language works237,207