MétaCan
Menu
Back to cohort

Are There Downsides to Mammography Screening?

2005· review· en· W2134183231 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Breast Journal · 2005
Typereview
Languageen
FieldMedicine
TopicGlobal Cancer Incidence and Screening
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsOverdiagnosisMedicineMammographyBreast cancerScreening mammographyMammography screeningGynecologyCancerRadiologyInternal medicine

Abstract

fetched live from OpenAlex

Most Americans clearly believe that routine screening mammography is beneficial. Given its widespread acceptance, it is useful to consider what the downsides of mammography screening are so that patients are fully informed in the decisions they make. This article lists some less well-recognized risks of mammography, such as false negatives and their accompanying false reassurance, as well as the direct and indirect costs to women and society. Three important downsides-radiation hazard, overdiagnosis of breast cancer, and the paradoxical increase in breast cancer mortality observed in screened women compared to controls age 40-49 years-are addressed. The article also considers the reasons that women are poorly informed about the downsides of mammography. There is, however, agreement that early diagnosis and treatment are important, and that new methods to reduce breast cancer deaths must be sought.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.979
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.155
GPT teacher head0.402
Teacher spread0.247 · 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