MétaCan
Menu
Back to cohort
Record W2006087930 · doi:10.1155/2012/545062

Worry Is Good for Breast Cancer Screening: A Study of Female Relatives from the Ontario Site of the Breast Cancer Family Registry

2012· article· en· W2006087930 on OpenAlexafffundabout
Li Zhang, Anna M. Chiarelli, Gord Glendon, Lucia Mirea, Julia A. Knight, Irene L. Andrulis, Paul Ritvo

Bibliographic record

VenueJournal of Cancer Epidemiology · 2012
Typearticle
Languageen
FieldMedicine
TopicGlobal Cancer Incidence and Screening
Canadian institutionsYork UniversityPublic Health OntarioUniversity of TorontoMount Sinai HospitalLunenfeld-Tanenbaum Research InstituteCancer Care Ontario
FundersNational Institutes of HealthCanadian Breast Cancer Research AllianceNational Cancer InstituteCancer Care Ontario
KeywordsWorryBreast cancerLogistic regressionAlgorithmMedicineCancerArtificial intelligenceComputer scienceInternal medicine

Abstract

fetched live from OpenAlex

Background. Few prospective studies have examined associations between breast cancer worry and screening behaviours in women with elevated breast cancer risks based on family history. Methods. This study included 901 high familial risk women, aged 23-71 years, from the Ontario site of the Breast Cancer Family Registry. Self-reported breast screening behaviours at year-one followup were compared between women at low (N = 305), medium (N = 433), and high (N = 163) levels of baseline breast cancer worry using logistic regression. Nonlinear relationships were assessed using likelihood ratio tests. Results. A significant non-linear inverted "U" relationship was observed between breast cancer worry and mammography screening (P = 0.034) for all women, where women at either low or high worry levels were less likely than those at medium to have a screening mammogram. A similar significant non-linear inverted "U" relationship was also found among all women and women at low familial risk for worry and screening clinical breast examinations (CBEs). Conclusions. Medium levels of cancer worries predicted higher rates of screening mammography and CBE among high-risk women.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.163
Threshold uncertainty score0.953

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.205
GPT teacher head0.426
Teacher spread0.221 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations27
Published2012
Admission routes3
Has abstractyes

Explore more

Same venueJournal of Cancer EpidemiologySame topicGlobal Cancer Incidence and ScreeningFrench-language works237,207