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Record W1676765679 · doi:10.1093/jnci/djv242

RE: Breast Cancer, Heart Disease, and Whispering “Fire” in a Public Theater

2015· letter· en· W1676765679 on OpenAlexaff
Norman F. Boyd, Lisa Martin

Bibliographic record

VenueJNCI Journal of the National Cancer Institute · 2015
Typeletter
Languageen
FieldMedicine
TopicGlobal Cancer Incidence and Screening
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsBreast cancerMedicineInternal medicineCancer

Abstract

fetched live from OpenAlex

We write in response to the editorial of Byers and Goff, who suggest that our recent paper describing an association between serum lipids, lipoproteins, and risk of breast cancer ( 1 ) is akin to “whispering fire in a public theatre” ( 2 ). It seems highly unlikely that our article, and the suggestion it contains to conduct further research into the relationship of lipids, and their modification, with risk of breast cancer, will discourage any from taking up the large expansion in the use of statins that has been proposed by the American Heart Association ( 3 ). To date, information on statin use and breast cancer risk is mostly reassuring ( 4 ), although a recent population-based study showed an increased risk of breast cancer with long-term use ( 5 ). As stated in our article, we have not yet excluded potential confounding by alcohol on the association of HDL-C and apolipoprotein A with risk of breast cancer. Nor have we excluded potential mediation by lipids of the effects of alcohol on breast cancer risk. The association of LDL-C and apolipoprotein B (Apo-B) with breast cancer risk is less likely to be explained by alcohol intake. A similar inverse association of Apo-B with risk of breast cancer was recently described in the Malmo Diet and Cancer Study ( 6 ). Work to further address these questions is in progress. Byers and Goff also suggest that our selection of subjects with mammographic density may introduce exposure to estrogen as an additional potential confounding variable. Neither of the papers they cite in support of this idea contains any evidence that mammographic density is associated with higher levels of endogenous estrogen exposure. Most studies to date of blood levels of ovarian hormones and mammographic density in adolescent or in adult premenopausal or postmenopausal women have found either no association or an inverse association. To our knowledge only one study to date in postmenopausal women found a positive association of serum levels of estradiol with mammographic density (reviewed in [ 7 ]). Randomized trials have shown that postmenopausal combined hormone therapy increases mammographic density but estrogen alone does not. Further, there are many factors with opposite effects on risk for cardiovascular disease and breast cancer. These include height, parity, age at menopause, and tamoxifen, which increases risk of venous thromboembolism and may increase risk of stroke but decreases risk of breast cancer. We appreciate Byers’ and Goff’s gracious agreement that our peer-reviewed paper merits publication and hope that their generosity will extend to tolerance of the spirit of free enquiry into the health effects of modifying lipid levels. Because of space constraints, we could not list all relevant references, but these are available on request from the authors.

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)0.003
metaresearch head score (Gemma)0.021
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 score0.071
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.021
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0090.003
Scholarly communication0.0050.005
Open science0.0020.003
Research integrity0.0710.058
Insufficient payload (model declined to judge)0.0170.009

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 head0.154
GPT teacher head0.372
Teacher spread0.219 · 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

Citations1
Published2015
Admission routes1
Has abstractno

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