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Record W2022571493 · doi:10.3390/cancers3010252

Rational and Irrational Issues in Breast Cancer Screening

2011· article· en· W2022571493 on OpenAlexaffabout
Cornelia J. Baines

Bibliographic record

VenueCancers · 2011
Typearticle
Languageen
FieldMedicine
TopicGlobal Cancer Incidence and Screening
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBreast cancerMedicineBreast cancer screeningTask forceRandomized controlled trialIrrational numberIntensive care medicineFamily medicineCancerPolitical scienceMammographyPathologyInternal medicinePublic administration

Abstract

fetched live from OpenAlex

Evidence on the efficacy of breast screening from randomized controlled trials conducted in the last decades of the 1900s is reviewed. For decades, controversy about their results has centered on the magnitude of benefit in terms of breast cancer mortality reduction that can be achieved. However more recently, several expert bodies have estimated the benefits to be smaller than initially expected and concerns have been raised about screening consequences such as over-diagnosis and unnecessary treatment. Trials with substantial mortality reduction have been lauded and others with null effects have been critiqued. Critiques of the Canadian National Breast Screening Study are refuted. Extreme responses by screening advocates to the United States Preventive Services Task Force 2009 guidelines are described. The role vested interests play in determining health policy is clearly revealed in the response to the guidelines and should be more generally known. A general reluctance to explore unexpected results or to accept new paradigms is briefly discussed.

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)0.295
metaresearch head score (Gemma)0.510
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.295
Threshold uncertainty score0.870

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2950.510
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0050.003
Science and technology studies0.0030.048
Scholarly communication0.0090.017
Open science0.0050.007
Research integrity0.0190.030
Insufficient payload (model declined to judge)0.0050.001

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 head0.102
GPT teacher head0.351
Teacher spread0.249 · 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.

Study designTheoretical or conceptual
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

Citations11
Published2011
Admission routes2
Has abstractyes

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