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Record W1814413585 · doi:10.1136/bmj.h3156

Experts question IARC report saying benefits of mammography in older women outweigh risks

2015· letter· en· W1814413585 on OpenAlexaboutno aff
S. Mayor

Bibliographic record

VenueBMJ · 2015
Typeletter
Languageen
FieldMedicine
TopicGlobal Cancer Incidence and Screening
Canadian institutionsnot available
Fundersnot available
KeywordsMammographyInternational agencyMedicineAgency (philosophy)Breast cancerFamily medicineConfoundingPublic healthMammography screeningGerontologyGynecologyCancerNursingPathologySociologySocial science

Abstract

fetched live from OpenAlex

Experts are questioning a summary report on breast screening developed by a panel for the World Health Organization’s International Agency for Research on Cancer that concluded that the benefits of mammography outweighed the risks in women aged 50-74.1 Critics consider that the summary, published ahead of the main report, failed to take account of bias and confounding factors in some screening studies and that the panel’s membership did not reflect varying views on the value of breast screening. The agency’s report, summarised in the New England Journal of Medicine last week,2 said that its conclusions represented the consensus of a panel of 29 invited experts after they discussed preliminary evaluations of available evidence developed by subgroups. It reported that the group found “sufficient” evidence that mammography reduced mortality from breast cancer in women aged 50-69 years of age and in women aged 70-74 years. But one member of the panel, Anthony Miller, emeritus professor at the University of Toronto’s Dalla Lana School of Public Health, was concerned that the report did not …

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)0.010
metaresearch head score (Gemma)0.066
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: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.067
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.066
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0040.004
Open science0.0020.002
Research integrity0.0670.040
Insufficient payload (model declined to judge)0.0110.016

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 head0.135
GPT teacher head0.395
Teacher spread0.259 · 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
GenreCommentary

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

Citations2
Published2015
Admission routes1
Has abstractyes

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