MétaCan
Menu
Back to cohort
Record W1764635065

Familial and hormonal risk factors for papillary serous uterine cancer.

2002· article· en· W1764635065 on OpenAlexaff
L. Elit, T. Pal, Ran Goshen, Helena Jernström, Ida Ackerman, Anthony Fyles, Mark Carey, Margot Mitchell, Jennifer Aubé, SA Narod

Bibliographic record

VenuePubMed · 2002
Typearticle
Languageen
FieldMedicine
TopicCancer Risks and Factors
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineBreast cancerEndometrial cancerCancerFamily historyOvarian cancerOncologyGynecologyProstate cancerSerous fluidUterine cancerInternal medicineEstrogenCase-control studyObstetrics
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVES: To identify genetic and non-genetic risk factors for papillary serous uterine cancer. METHODS: A case-control study was conducted. Case women with papillary serous uterine cancer were compared with two control groups: 1) women with endometrioid uterine cancer and 2) healthy women with no past history of cancer. Cases and controls were matched for age (within two years) and ethnic group. All study subjects completed a questionnaire addressing family history. The cases and healthy controls were assessed for factors associated with estrogen exposure. RESULTS: The risks of breast cancer (RR 1.84, CI 1.03-3.31) and of prostate cancer (RR 2.21, CI 0.77-6.37) were higher among the relatives of patients with papillary serous uterine cancer, than among relatives of those with endometrioid uterine cancer. Other significant risk factors included weight at 18 years (p = 0.04) and the use of estrogen replacement therapy (p = 0.04). CONCLUSION: Relatives of women with papillary serous cancer of the uterus had an increased risk of breast and prostate cancer. Hormonal exposure also increases the risk for this cancer. These findings suggest that predisposing genetic factors, possibly related to hormone metabolism, may be common to the three forms of cancer.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.632
Threshold uncertainty score0.436

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.044
GPT teacher head0.264
Teacher spread0.220 · 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

Citations6
Published2002
Admission routes1
Has abstractyes

Explore more

Same venuePubMedSame topicCancer Risks and FactorsFrench-language works237,207