Breast Cancer in Young Women: A Clinicopathological Hospital-based Descriptive Study from Kurdistan, Iraq
Bibliographic record
Abstract
Background: Young women with breast cancer have been reported to present more aggressive clinical and pathological features, requiring more treatment options compared with older patients. Our objective was to investigate the clinicopathological features of breast cancer in our local young women. Method: We conducted an observational descriptive study on 100 young women (age ≤ 40) with breast cancer. The subjects were taken care of, at a single tertiary cancer facility, from mid-2007 to mid-2014. We reviewed the clinicopathological profiles and therapeutic strategies. Results: Ratio of breast cancer in young women was about 13% of all breast cancer patients. The mean age of the patients was 35 years ± 4SD. 56% of the patients had grade III tumors and 46% were in stage III. Hormonal receptors were positive in 70%, while HER2 was positive in 26%. 70% of the patients underwent modified radical mastectomy, 96% received chemotherapy, and 70% received radiotherapy and required hormonal therapy. Conclusion: This review showed that breast cancer in our local young women was largely diagnosed at advanced stages with more aggressive clinico-pathological features. Moreover, most of the patients received more aggressive treatment options. Therefore, physicians should pay a close attention to breast lumps in young women.
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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 | 0.001 | 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".