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Breast cancer screening program in canada: successes and challenges

2009· article· en· W2139543413 on OpenAlexaffabout
Verna Mai, Terrence Sullivan, Anna M. Chiarelli

Bibliographic record

VenueSalud Pública de México · 2009
Typearticle
Languageen
FieldMedicine
TopicGlobal Cancer Incidence and Screening
Canadian institutionsCancer Care Ontario
Fundersnot available
KeywordsBreast cancer screeningMedicineBreast cancerFamily medicineBreast screeningHealth careMammographyEconomic growthCancer

Abstract

fetched live from OpenAlex

This paper describes breast screening program development in Canada and the current status of screening in Canada. Programs have been implemented in most of Canada, beginning in the late 1980's. Certain components are common to all the programs, but others, such as personal invitation letters for recruitment and clinical breast examination vary across the country. Key successes in organized breast screening in Canada include the development of a comprehensive set of screening performance indicators, which are reported on regularly, and the downward trend in mortality rates observed over the past 20 years. Challenges include the continued prevalence of opportunistic screening; the need to better manage follow-up; dealing with changing evidence; and supporting informed decision-making about screening. Approaches to breast screening are dependent on the health care services available in countries, but regardless of the approach, good evaluation is necessary.

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)0.009
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.917
Threshold uncertainty score0.600

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.006
Science and technology studies0.0070.002
Scholarly communication0.0050.002
Open science0.0030.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.065
GPT teacher head0.331
Teacher spread0.266 · 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 designNot applicable
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

Citations40
Published2009
Admission routes2
Has abstractyes

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