Importance of hereditary and selected environmental risk factors in the etiology of inflammatory breast cancer: a case-comparison study
Bibliographic record
Abstract
BACKGROUND: To assess the importance of heredity in the etiology of inflammatory breast cancer (IBC), we compared IBC patients to several carefully chosen comparison groups with respect to the prevalence of first-degree family history of breast cancer. METHODS: IBC cases (n = 141) were compared to non-inflammatory breast cancer cases (n = 178) ascertained through George Washington University (GWU) with respect to the prevalence of first-degree family history of breast cancer and selected environmental/lifestyle risk factors for breast cancer. Similar comparisons were conducted with subjects from three case-control studies: breast cancer cases (n = 1145) and unaffected controls (n = 1142) from the Cancer Genetic Markers of Susceptibility (CGEMS) study, breast cancer cases (n = 465) and controls (n = 9317) from the Women's Health Initiative (WHI) study, and ovarian cancer cases (n = 260) and controls (n = 331) from a study by University of Toronto (UT). RESULTS: The frequency of first-degree breast cancer family history among IBC cases was 17.0 % compared to 24.4 % for GWU breast cancer cases, 23.9 % and 17.9 % for CGEMS breast cancer cases and controls, respectively, 16.9 % and 12.6 % for WHI breast cancer cases and controls, respectively, and 24.2 % and 11.2 % for UT ovarian cancer cases and controls, respectively. IBC cases had a significantly lower prevalence of parous women than WHI breast cancer cases (OR = 0.46, 95 % CI:0.27-0.81) and controls (OR = 0.31, 95 % CI:0.20-0.49). Oral contraceptive use was significantly higher among IBC cases compared to WHI breast cancer cases (OR = 7.77, 95 % CI:4.82-12.59) and controls (OR = 8.14, 95 % CI:5.28-12.61). IBC cases had a significantly higher frequency of regular alcohol consumption (≥1 drink per day) compared to WHI controls (OR = 1.84, 95 % CI:1.20-2.82) and UT controls (OR = 1.86, 95 % CI:1.07-3.22) and higher (statistically non-significant) prevalence (21.3 %) compared to breast cancer cases from GWU (18.2 %) and WHI (15.2 %). CONCLUSIONS: The prevalence of first-degree breast cancer family history among IBC cases was lower compared to breast and ovarian cancer cases but higher than unaffected individuals. Our multiple-case inflammatory and non-inflammatory breast cancer families may reflect aggregation of common genetic and/or environmental factors predisposing to both types of breast cancer. Our findings that oral contraceptive use and regular alcohol consumption may be associated with IBC warrant further investigations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".