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Record W4220723990 · doi:10.1093/jbi/wbab099

The Fundamental Flaws of the CNBSS Trials: A Scientific Review

2022· review· en· W4220723990 on OpenAlexaffabout
Jean M. Seely, Peter R. Eby, Martin J. Yaffe

Bibliographic record

VenueJournal of Breast Imaging · 2022
Typereview
Languageen
FieldMedicine
TopicGlobal Cancer Incidence and Screening
Canadian institutionsSunnybrook HospitalUniversity of TorontoOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsMammographyMedicineBreast cancerRandomized controlled trialContext (archaeology)Breast cancer screeningRandomizationPopulationBreast imagingMedical physicsGynecologyCancerInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

Although the two Canadian National Breast Screening Study (CNBSS) trials were performed 40 years ago, their negative findings continue to heavily influence screening policies around the world. These policies, based on underestimates of the mortality reduction attributable to mammography particularly for women in the 40-49-year age range, contribute to increased mortality and morbidity from breast cancer. This review summarizes principles of a randomized controlled trial (RCT) and evaluates the compliance of the CNBSS1 and CNBSS2 RCTs in the context of these principles. We describe the fundamental flaws of the CNBSS trials, which failed to demonstrate mortality benefit of screening mammography and contribute to their being the only two outlier studies of eight screening mammography RCTs. The most significant flaws of the trials are (1) inadequate power to detect significant differences in breast cancer mortality; (2) very poor quality mammography with low sensitivity and cancer detection rates; (3) inclusion of women with symptoms of breast cancer; and (4) study design that allowed for violation of the randomization of the allocation process. Finally, we demonstrate that the conditions of the screening intervention in the CNBSS do not reflect the environment of modern population-based screening mammography programs.

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)0.010
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.972
Threshold uncertainty score0.470

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.279
GPT teacher head0.469
Teacher spread0.190 · 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 designOther design
Domainnot available
GenreReview

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

Citations18
Published2022
Admission routes2
Has abstractyes

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