Distribution of Breast Cancer Biomarkers by Age in Iran
Bibliographic record
Abstract
Background and Objectives: Breast cancer is the leading cause of cancer related death globally and presents as the most common female malignancy in Iran. Multiple factors are associated with an increased risk of developing breast cancer; for example first degree family history of breast cancer, BRCA1, 2 mutation and history of atypical hyperplasia on biopsy are the most important risk factors for developing breast cancer. Some prognostic factors are classically used that it would help us to either choosing recommended optimal treatment or recognizing the prognosis. In several studies it is shown that these factors have different patterns in age groups or histopathologic types. The aim of this study was to determine the age distribution of hormone receptors and biomarkers and determine their relation to the histopathologic types. Methods: Data were gathered from the medical records of Baqiyatallah hospital, Tehran, Iran. Breast cancer patients whose disease was confirmed by pathologic studies and had immunohistochemical profile, were included. Estrogen receptor (ER), Progesterone receptors (PR), HER2/neu and p53 were selected as biomarkers of this study. Results: Mean age of patients was 49.47±12.50 years (range 20 to 86). The most common histopathologic type was invasive ductal carcinoma. Distribution of ER and PR against age detected similar; ER positivity increased with age and it peaked in fifth decade of life, and PR positivity showed more regular pattern and it also peaked in fifth decade of life (p <0.05) HER2/neu positivity also had trend to increase with age and it peaked in sixth decade of life, but P53 had trend to show unimodal distribution pattern that peaked in sixth decade of life, but this findings were not statistically significant (p>0.05). Conclusions: Our breast cancer patients were generally younger than patients round the world. The different distribution pattern of biomarkers in our studies in comparison with similar studies, may suggest different biologic behavior of breast cancer in our patients. Further studies will help illuminate this point.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".