Ethnic variation in kallikrein expression in nipple aspirate fluid
Bibliographic record
Abstract
Socioeconomic factors cannot entirely explain why black women have an earlier age of breast cancer onset and higher mortality rates, stage for stage, than whites. We and others have shown that prostate-specific antigen [PSA, also known as human kallikrein (hK) 3] is a marker of breast as well as prostate cancer, that hK2 and hK3 are highly homologous at the DNA and protein level and that the level of progesterone, which appears to upregulate hK3, is influenced by ethnicity. We hypothesized that nipple aspiration fluid (NAF) hK2 and hK3 levels are (i) lower in black than white women; (ii) independently associated with breast cancer; (iii) influenced by menopausal status; and (iv) in combination are more informative about whether a woman has breast cancer than either marker alone. NAF was assayed for hK2 and hK3, and the results were stratified by ethnicity, presence or absence of cancer and menopausal status. Statistical analysis was then performed. When stratified by ethnicity, hK2 (p = 0.003) and hK3 (p = 0.027) levels in blacks were lower than in whites. hK2 was lower in premenopausal black than in white subjects, regardless of cancer status. Overall, hK2, hK3 and the ratio hK2/hK3 were lower in subjects with breast cancer than in normal subjects. hK3 was lower in postmenopausal women with breast cancer, regardless of ethnicity. hK2 and hK3 levels were higher in pre- than in postmenopausal whites. Using logistic regression and considering hK2, hK3, hK2/hK3 and ethnicity, hK3 was significantly associated with breast cancer in both pre- (p < 0.001) and postmenopausal women (p = 0.023). In conclusion, whereas hK2, hK3, hK2/hK3 and ethnicity are each significantly associated with breast cancer bivariately, after entering the strongest predictor, hK3, into a logistic regression model, no other variable accounted for additional variation, although this observation is preliminary due to the limited number of black subjects in the study.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".