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Record W1983386859 · doi:10.1093/jnci/djv094

RE: Pan-Canadian Study of Mammography Screening and Mortality From Breast Cancer

2015· letter· en· W1983386859 on OpenAlexaffabout
Steven A. Narod, Vasily Giannakeas, Anthony B. Miller

Bibliographic record

VenueJNCI Journal of the National Cancer Institute · 2015
Typeletter
Languageen
FieldMedicine
TopicGlobal Cancer Incidence and Screening
Canadian institutionsPublic Health OntarioWomen's College Hospital
Fundersnot available
KeywordsMammographyMedicineBreast cancerMammography screeningCancerGynecologyObstetricsOncologyInternal medicine

Abstract

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We were struck by the large reduction in breast cancer mortality reported by Coldman et al. in association with participation in Canadian mammography screening programs ( 1 ), in particular with the consistency in the sizes of the observed reductions across all provinces and across all age groups. Overall, they reported a 40% reduction in breast cancer mortality associated with ever participating in a provincial breast screening program. We conducted a cohort-based analysis of women enrolled in the Canadian National Breast Screening study (CNBSS) and found that screening initiated before age 50 years was not associated with a decline in mortality before age 60 years ( 2 ). In our study, the hazard ratio for death from breast cancer associated with entry into the screening program was 1.10 (95% confidence interval [CI] = 0.86 to 1.40). The screening period in our study was earlier (1980 to 1985) than in the Coldman study (1990 to 2009) and the age groups were different, but most importantly, in the CNBSS, screening was assigned at random, whereas in the Coldman study the screened women were volunteers. The reduction in mortality cannot readily be explained by confounding (‘healthy screening’ bias) because comorbidity and lifestyle do not have sufficient impact on outcome after a diagnosis of breast cancer such that they could explain such a profound mortality difference. Selection bias is a greater concern ( 3 ). We excluded women with a past history of breast cancer from both subcohorts, and we excluded women who had a recent mammogram. If a woman has been diagnosed with breast cancer in the past she will not be a candidate to enroll in a breast cancer screening program. For example, a woman might have been diagnosed in 1989 and die in 1995. In an observational study, her person-years (from 1990 to 1995) and death (in 1995) would be counted among the unscreened women, unless women with prior cancer were specifically excluded. Consider a woman who had breast screening prior to 1990; if she had cancer she would then be counted in the nonscreened cohort, and if she didn’t have cancer she would be eligible to participate in the screening program. In the latter case, her a priori risk of cancer would be reduced, as would her risk of dying of cancer. For this reason, we excluded women who had a recent screen from the CNBSS study from the outset. Of note, in the Pan-Canadian study, 45% of Ontario nonparticipants had a screen outside the program and yet the hazard ratio was still strongly protective (hazard ratio = 0.73, 95% CI = 0.68 to 0.78). We do not know how many women had a mammogram prior to entry. To alleviate our concern that selection bias influenced the results of their study, it would be helpful if Coldman et al. ( 1 ) would provide the dates of diagnosis of breast cancer for the women who died of cancer during the study period. Also, if bias were present, we should see a more extreme protective effect for the first decade (1990–1999) than for the second decade (2000–2009).

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)0.002
metaresearch head score (Gemma)0.005
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: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.034
Threshold uncertainty score0.246

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.010
Science and technology studies0.0060.001
Scholarly communication0.0020.001
Open science0.0030.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0120.002

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 head0.178
GPT teacher head0.391
Teacher spread0.213 · 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

Citations4
Published2015
Admission routes2
Has abstractyes

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